EPSRC Centre for Mathematical and Statistical Analysis of Multimodal Clinical Imaging
Lead Research Organisation:
University of Cambridge
Department Name: Pure Maths and Mathematical Statistics
Abstract
Applied Mathematics and Statistics are routinely seen as separate disciplines. However, many of the methodological challenges in image analysis, particularly from the types of multiscale multimodal images available from Neurological, Cardiovascular and Oncology imaging, illustrate that a combined approach, dissolving intradisciplinary mathematical boundaries, is the only possible way forward if a step-change in image analysis of combined data is to occur. This Centre will foster links between applied maths and statistics, particularly high dimensional and functional statistical analysis with applied and computational numerical analysis, through the focus on multimodal imaging data. The Centre proposes to provide a research focus on bringing state-of-the-art mathematical tools to clinical end users through collaborations both within mathematics and between mathematics and healthcare professionals, particularly those in oncology, cardiovascular medicine and neurology. This will ultimately lead to new mathematical frontiers, joining statistics and computational mathematics, as well as a move away from individual image analysis to a holistic approach to all available imaging, from the cellular to systems scale, for clinical diagnosis, prognosis and treatment planning.
Planned Impact
The Centre will focus on making an impact within mathematical sciences as well as delivering breakthroughs in image analysis for a number of critical healthcare areas. We will channel efforts of researchers in applied mathematics and statistics to build a common understanding of their (currently disparate) disciplines. Such a shift requires a common focus, and by concentrating efforts on clinical image analysis, such a goal can be realised. The advances generated by the Centre will go well beyond mathematical sciences, to directly influence clinical practice. Mathematicians, statisticians, clinicians and scientists with track records in turning theoretical advances into clinical practice will incorporate the most modern analysis techniques into clinical protocols, taking a multimodal, holistic approach to all available imaging data. By combining imaging data with other clinical covariates such as digitised medical records, clinicians will come to better informed decisions regarding diagnosis, prognosis and treatment options.
We will concentrate on three specific medical areas containing some of the most pressing needs in medicine. Oncology imaging ranges from tumour cell images, to histopathology, to whole organ MRI and PET scans, and we will build models which integrate such disparate data into a single diagnosis or prognosis framework. Cardiovascular imaging has particular issues due to the inherent movement of the heart. We will generate considerably enhanced real time imaging solutions for cardiac imaging, with potential advances, particularly in diagnosis. We will improve on the use of neurological imaging data for diseases such as Alzheimer's and dementia. Moreover, in clinical populations with traumatic brain injury or disorders of consciousness, in-vivo neurological imaging goes beyond simple diagnosis and becomes part of the "treatment", to interact directly with patients who have little to no other form of communication available. For this we will develop accurate statistical prediction and classification models, along with computational methods, allowing them to be used in real time.
The model of engagement at the heart of the centre will clearly demonstrate the benefits to researchers of working together on problems of mutual interest. Successful collaborations between the physical and clinical sciences require that academic rigour and clinical relevance are well-aligned, with projects selected that offer the potential to develop truly exciting and cutting-edge mathematics, while addressing areas of medicine in which these advances are most needed (rather than only offering incremental clinical benefit). Our model for the co-creation of projects with mathematics and clinical leads is, we believe, fundamental to the development of genuinely challenging and relevant outputs, and will provide a model for these types of engagement in the future.
Developments in healthcare technology have widespread impacts beyond academic research. Improvements in diagnostic and prognostic tools, and the prospect of integrating real-time image analysis into treatments, can provide a step change in care, improving outcomes and reducing cost. Beneficiaries will include patients, clinicians, hospitals, the NHS (and other healthcare services worldwide) and ultimately taxpayers. Technology companies will benefit from our innovations - both our immediate partners via IP developed as part of the Centre, but also the wider healthcare tech community, through our publications, IP licensing and the open-source software tools we are committed to developing. Our outputs will deliver further academic and economic impact by training high-calibre researchers with expertise and skills relevant to the new cross-disciplinary data science challenges of the 21st century. We will engage early and often with policymakers (e.g. in the Department of Health and NICE), and with the public, demonstrating the deep relevance of mathematics to health.
We will concentrate on three specific medical areas containing some of the most pressing needs in medicine. Oncology imaging ranges from tumour cell images, to histopathology, to whole organ MRI and PET scans, and we will build models which integrate such disparate data into a single diagnosis or prognosis framework. Cardiovascular imaging has particular issues due to the inherent movement of the heart. We will generate considerably enhanced real time imaging solutions for cardiac imaging, with potential advances, particularly in diagnosis. We will improve on the use of neurological imaging data for diseases such as Alzheimer's and dementia. Moreover, in clinical populations with traumatic brain injury or disorders of consciousness, in-vivo neurological imaging goes beyond simple diagnosis and becomes part of the "treatment", to interact directly with patients who have little to no other form of communication available. For this we will develop accurate statistical prediction and classification models, along with computational methods, allowing them to be used in real time.
The model of engagement at the heart of the centre will clearly demonstrate the benefits to researchers of working together on problems of mutual interest. Successful collaborations between the physical and clinical sciences require that academic rigour and clinical relevance are well-aligned, with projects selected that offer the potential to develop truly exciting and cutting-edge mathematics, while addressing areas of medicine in which these advances are most needed (rather than only offering incremental clinical benefit). Our model for the co-creation of projects with mathematics and clinical leads is, we believe, fundamental to the development of genuinely challenging and relevant outputs, and will provide a model for these types of engagement in the future.
Developments in healthcare technology have widespread impacts beyond academic research. Improvements in diagnostic and prognostic tools, and the prospect of integrating real-time image analysis into treatments, can provide a step change in care, improving outcomes and reducing cost. Beneficiaries will include patients, clinicians, hospitals, the NHS (and other healthcare services worldwide) and ultimately taxpayers. Technology companies will benefit from our innovations - both our immediate partners via IP developed as part of the Centre, but also the wider healthcare tech community, through our publications, IP licensing and the open-source software tools we are committed to developing. Our outputs will deliver further academic and economic impact by training high-calibre researchers with expertise and skills relevant to the new cross-disciplinary data science challenges of the 21st century. We will engage early and often with policymakers (e.g. in the Department of Health and NICE), and with the public, demonstrating the deep relevance of mathematics to health.
Organisations
- University of Cambridge (Lead Research Organisation)
- Cambridge Carbon Capture Ltd (Collaboration)
- Friedrich-Alexander University Erlangen-Nuremberg (Collaboration)
- Schlumberger Limited (Collaboration)
- Shanghai Jiao Tong University (Collaboration)
- DeepMind Health (Collaboration)
- Norwegian University of Science and Technology (NTNU) (Collaboration)
- La Trobe University (Collaboration)
- QUEEN MARY UNIVERSITY OF LONDON (Collaboration)
- Cancer Research UK Cambridge Institute (Collaboration)
- École Polytechnique (Collaboration)
- University of Bath (Collaboration)
- Beijing Institute of Technology (Collaboration)
- Icahn School of Medicine at Mount Sinai (Collaboration)
- Chinese University of Hong Kong (Collaboration)
- Fudan University (Collaboration)
- University College London (Collaboration)
- University of Manchester (Collaboration)
- National Institute of Applied Sciences of Rouen (Collaboration)
- Peking University (Collaboration)
- UNIVERSITY OF CAMBRIDGE (Collaboration)
- IMPERIAL COLLEGE LONDON (Collaboration)
- University of Osnabrück (Collaboration)
- Duke University (Collaboration)
- UNIVERSITY OF GLASGOW (Collaboration)
- University of Lyon (Collaboration)
- GE Healthcare Limited (Collaboration)
- Johnson & Johnson (Collaboration)
- PreXion Corporation (Collaboration)
- Saarland University (Collaboration)
- UNIVERSITY OF EDINBURGH (Collaboration)
- Luebeck University of Applied Sciences (Collaboration)
- Royal Institute of Technology (Collaboration)
- King Abdullah University of Science and Technology (KAUST) (Collaboration)
- CLK GmBH (Collaboration)
- Monash University (Collaboration)
- Siemens plc (UK) (Project Partner)
- Cambridge University Hospitals NHS Foundation Trust (Project Partner)
- Cambridge Computed Imaging Ltd (Project Partner)
- Microsoft Research (United Kingdom) (Project Partner)
- AstraZeneca (United Kingdom) (Project Partner)
Publications
Zhong P
(2017)
Learning to Diversify Deep Belief Networks for Hyperspectral Image Classification
in IEEE Transactions on Geoscience and Remote Sensing
Zheng JS
(2017)
Maternal Blood Pressure Rise During Pregnancy and Offspring Obesity Risk at 4 to 7 Years Old: The Jiaxing Birth Cohort.
in The Journal of clinical endocrinology and metabolism
Zhao T
(2020)
Low dose interleukin-2 in patients with stable ischaemic heart disease and acute coronary syndrome (LILACS)
in European Heart Journal
Zhang Y
(2021)
Biomechanical insight of the stent-induced thrombosis following flow-diverting strategy in the management of complicated aortic aneurysms.
in International angiology : a journal of the International Union of Angiology
Zhang Jianchao
(2019)
Dynamic Spectral Residual Superpixels
in arXiv e-prints
Zhang J
(2021)
Dynamic spectral residual superpixels
in Pattern Recognition
Yoon J
(2020)
A background correction method to compensate illumination variation in hyperspectral imaging.
in PloS one
Yoon J
(2021)
First experience in clinical application of hyperspectral endoscopy for evaluation of colonic polyps.
in Journal of biophotonics
Yoon J
(2019)
A clinically translatable hyperspectral endoscopy (HySE) system for imaging the gastrointestinal tract.
in Nature communications
Yin Yingda
(2019)
Deep Reflection Prior
in arXiv e-prints
Yin Y
(2020)
Deep Reflection Prior
Yeung M
(2021)
Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy.
in Computers in biology and medicine
Yeung M
(2022)
Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation
in Computerized Medical Imaging and Graphics
Wu J
(2021)
Radiological tumor classification across imaging modality and histology.
in Nature machine intelligence
Williams J
(2020)
3D Segmentation of Trees Through a Flexible Multiclass Graph Cut Algorithm
in IEEE Transactions on Geoscience and Remote Sensing
Weir-Mccall J
(2019)
P2237National trends in coronary artery disease imaging
in European Heart Journal
Weir-Mccall J
(2022)
NATIONAL TRENDS IN NON-INVASIVE CORONARY ARTERY DISEASE IMAGING:ASSOCIATIONS WITH HEALTH CARE OUTCOMES
in Journal of the American College of Cardiology
Weir-McCall J
(2020)
Impact Of Training And Hardware Availability On Uptake Of Coronary Computed Tomography In Response To Guidelines
in Journal of Cardiovascular Computed Tomography
Wei, Kaixuan And Aviles-Rivero, Angelica And Liang, Jingwei And Fu, Ying And Sch{\"o}nlieb, Carola-Bibiane And Huang, Hua
(2020)
Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems
Wei Y
(2023)
Structural connectome quantifies tumour invasion and predicts survival in glioblastoma patients.
in Brain : a journal of neurology
Wei T
(2022)
Beyond fine-tuning: Classifying high resolution mammograms using function-preserving transformations.
in Medical image analysis
| Title | Creative Reactions |
| Description | Inspired by our research, a local artist produced a creative reaction to our Pint of Science presentation |
| Type Of Art | Artwork |
| Year Produced | 2017 |
| Impact | Artist was featured on local TV |
| Title | Making stuff disappear using applied mathematics (video) |
| Description | A fun video showing various methods for removing items from a video using applied mathematics |
| Type Of Art | Film/Video/Animation |
| Year Produced | 2019 |
| Impact | This video will reach an international audience through YouTube, engaging researchers, industry and the general public. To date over 1300 people have viewed the video. |
| URL | https://www.youtube.com/watch?v=-yfApxV62hw&feature=youtu.be |
| Description | This research is primarily about linking mathematics and statistics to modern healthcare problems. We have seen that mathematical challenges arise in almost all areas of medical and healthcare imaging, and combining data in all its forms together to make the most of a patient's own data, is one of the premier research challenges of our time. We have looked at biomechanical models for imaging the heart, statistical models for understanding the brain, and combined maths and statistics models for getting the most out of the whole imaging process. We feel it is increasingly understood that medical data really needs both doctors and quantitative scientists to be involved in the analysis, to really help patients get the best diagnosis and prognosis possible. Various projects within the centre have shown the benefits of linking applied mathematics with statistics to address these challenges, and the work of the centre to date has highlighted the importance of both specialties working alongside both clinicians and industry in this field. Also, a recent prominent spin-off of the Centre is the work that we are doing within the project on AI for COVID-19 diagnosis and prognosis, more details here https://covid19ai.maths.cam.ac.uk |
| Exploitation Route | We feel that we have the opportunity to allow health professionals right across the board make use of some of the most cutting edge data analytics procedures, and we are excited for the engagements we have both now and those we are now planning for the future. Algorithms produced by the Centre's research team are provided open source. |
| Sectors | Digital/Communication/Information Technologies (including Software),Healthcare,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology |
| URL | https://archive.cmih.maths.cam.ac.uk |
| Description | The CMIH is establishing itself as a focal point for engagement between academia, clinicians and companies. Our events are now routinely attended by all of these groups. These events have provided a much needed networking space to bring together these usually very separate groups, enabling a better understanding of the challenges within the field, and a better appreciation of the relevant academic research. Working with industry we have assisted them in connecting with both academics and healthcare professionals, providing indirect benefits to them as companies. As the work of the centre grows, the benefits to both named industry partners, and to other industry contacts who connect with the centre will continue to grow. The benefits the centre can provide in the field of healthcare benefits to the UK as a whole, through improved health services, as well as economically through the associated benefits to industry. |
| Sector | Digital/Communication/Information Technologies (including Software),Healthcare,Pharmaceuticals and Medical Biotechnology |
| Impact Types | Societal |
| Description | PhD student training within the Cantab Capital Institute of the Mathematics of Information |
| Geographic Reach | Multiple continents/international |
| Policy Influence Type | Influenced training of practitioners or researchers |
| URL | http://www.damtp.cam.ac.uk/user/cbs31/MoI/Welcome.html |
| Description | All in one cancer imaging optimisation using an integrated mathematical and deep learning approach |
| Amount | £821,831 (GBP) |
| Organisation | Wellcome Trust |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 01/2020 |
| End | 12/2021 |
| Description | Alliance for Cancer Early Detection Cambridge Member Centre |
| Amount | £3,300,000 (GBP) |
| Organisation | Cancer Research UK |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 04/2019 |
| End | 03/2024 |
| Description | Cambridge Mathematics of Information in Healthcare (CMIH) |
| Amount | £1,295,778 (GBP) |
| Funding ID | EP/T017961/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 08/2020 |
| End | 08/2023 |
| Description | Can we exploit breast cancer sodium for diagnostic and therapeutic benefit? |
| Amount | £200,630 (GBP) |
| Funding ID | C57745/A25922 |
| Organisation | Cancer Research UK |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 01/2018 |
| End | 06/2019 |
| Description | Combining Knowledge And Data Driven Approaches to Inverse Imaging Problems |
| Amount | £1,240,288 (GBP) |
| Funding ID | EP/V029428/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 06/2021 |
| End | 05/2026 |
| Description | EPSRC Healthcare Impact Partnership: PET++: Improving Localisation, Diagnosis and Quantification in Clinical and Medical PET Imaging with Randomised Optimisation |
| Amount | £1,000,000 (GBP) |
| Funding ID | EP/S026045/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 09/2019 |
| End | 08/2022 |
| Description | EPSRC project EP/R008272/1 Microscopy with neutral helium atoms: A wide-ranging new technique for delicate samples |
| Amount | £1,100,000 (GBP) |
| Funding ID | EPSRC project EP/R008272/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 11/2017 |
| End | 10/2021 |
| Description | Industrial funding |
| Amount | £61,000 (GBP) |
| Organisation | PreXion |
| Sector | Private |
| Country | United States |
| Start | 03/2017 |
| End | 12/2017 |
| Description | Isaac Newton Institute research programme on Variational methods and effective algorithms for imaging and vision |
| Amount | £80,000 (GBP) |
| Organisation | Isaac Newton Institute for Mathematical Sciences |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 08/2017 |
| End | 12/2017 |
| Description | Isaac Newton Programme on Statistical Scalability |
| Amount | £180,000 (GBP) |
| Funding ID | Statistical Scalability |
| Organisation | Isaac Newton Institute for Mathematical Sciences |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 01/2018 |
| End | 06/2018 |
| Description | Kronecker Products for Imaging and Genetics |
| Amount | £128,000 (GBP) |
| Organisation | University of Cambridge |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 03/2017 |
| End | 03/2019 |
| Description | LMS Undergraduate Research Bursary for Bilevel optimisation for learning the sampling pattern in Magnetic Resonance Tomography |
| Amount | £1,400 (GBP) |
| Organisation | London Mathematical Society |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 07/2016 |
| End | 09/2016 |
| Description | LMS Undergraduate Research Bursary for Bilevel optimisation for learning the sampling pattern in Magnetic Resonance Tomography, summer 2016 |
| Amount | £1,400 (GBP) |
| Organisation | London Mathematical Society |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 07/2016 |
| End | 09/2016 |
| Description | MRC MB PhD funding for Elizabeth Le |
| Amount | £30,000 (GBP) |
| Organisation | Medical Research Council (MRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 10/2017 |
| End | 10/2020 |
| Description | MSCA-RISE-2015 - Marie Sklodowska-Curie Research and Innovation Staff Exchange (RISE): CHiPS CHallenges in Preservation of Structure |
| Amount | € 387,000 (EUR) |
| Funding ID | 691070 |
| Organisation | European Commission H2020 |
| Sector | Public |
| Country | Belgium |
| Start | 01/2016 |
| End | 12/2019 |
| Description | MSCA-RISE-2017 - Research and Innovation Staff Exchange: NoMADS: Nonlocal Methods for Arbitrary Data Sources |
| Amount | € 1,111,500 (EUR) |
| Funding ID | 777826 |
| Organisation | European Commission H2020 |
| Sector | Public |
| Country | Belgium |
| Start | 03/2018 |
| End | 02/2022 |
| Description | Mark Foundation Institute for Integrated Cancer Medicine |
| Amount | £8,600,000 (GBP) |
| Organisation | University of Cambridge |
| Department | The Mark Foundation Institute for Integrated Cancer Medicine |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 07/2018 |
| Description | Mark Foundation Institute for Integrative Cancer Medicine (MI2CM) |
| Amount | £8,600,000 (GBP) |
| Organisation | University of Cambridge |
| Department | The Mark Foundation Institute for Integrated Cancer Medicine |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 11/2017 |
| End | 10/2020 |
| Description | Mathematical and Statistical Theory of Imaging |
| Amount | £146,400 (GBP) |
| Organisation | University of Cambridge |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 01/2017 |
| End | 12/2018 |
| Description | NPL postdoctoral fellowship grant for The mathematics of measurement |
| Amount | £250,000 (GBP) |
| Organisation | National Physical Laboratory |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 04/2018 |
| End | 03/2021 |
| Description | Parke Davis Travelling Fellowship |
| Amount | £30,000 (GBP) |
| Organisation | Pfizer Ltd |
| Department | Parke Davis |
| Sector | Private |
| Country | Global |
| Start | 11/2017 |
| End | 11/2020 |
| Description | Philip Leverhulme Prize - Mathematical and Statistical Sciences 2017 |
| Amount | £100,000 (GBP) |
| Funding ID | https://www.leverhulme.ac.uk/philip-leverhulme-prizes/deep-inversion---towards-adaptive-physical-inversion-models-imaging |
| Organisation | The Leverhulme Trust |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 11/2018 |
| End | 10/2020 |
| Description | Postdoctoral Fellowship in The Mathematics of Information |
| Amount | £50,000 (GBP) |
| Organisation | Cognizant Technology Solutions |
| Sector | Private |
| Country | United States |
| Start | 05/2019 |
| End | 04/2020 |
| Description | Predicting Dementia: Optimising and translating AI to improve prognosis and clinical pathways |
| Amount | £764,285 (GBP) |
| Funding ID | 221633/Z/20/Z |
| Organisation | Wellcome Trust |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 02/2022 |
| End | 01/2025 |
| Description | Programme Grant |
| Amount | £2,750,890 (GBP) |
| Funding ID | EP/N031938/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 06/2016 |
| End | 05/2022 |
| Description | Revolutionizing Medical Imaging (ReImagine) through Ubiquitous, Low-Dose, Automated Computed Tomography Diagnostic Systems |
| Amount | £302,379 (GBP) |
| Funding ID | EP/W004445/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 09/2021 |
| End | 12/2022 |
| Description | Robust and Efficient Analysis Approaches of Remote Imagery for Assessing Population and Forest Health in India |
| Amount | £552,554 (GBP) |
| Funding ID | EP/T003553/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 09/2019 |
| End | 09/2021 |
| Description | The Mathematics of Deep Learning |
| Amount | £3,357,501 (GBP) |
| Funding ID | EP/V026259/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 08/2021 |
| End | 08/2026 |
| Description | The PILL Study - PET/MR vascular intervention |
| Amount | £44,920 (GEL) |
| Organisation | General Electric |
| Sector | Private |
| Country | United States |
| Start | 03/2019 |
| End | 03/2020 |
| Description | Turing seed funding for Personalized Breast Cancer Screening |
| Amount | £24,000 (CLF) |
| Organisation | Alan Turing Institute |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 10/2017 |
| End | 04/2018 |
| Description | Wellcome Trust Clinical Research Career Development Fellowship for Jason Tarkin |
| Amount | £672,549 (GBP) |
| Funding ID | 211100/Z/18/Z |
| Organisation | Wellcome Trust |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 04/2019 |
| End | 04/2024 |
| Description | rapiD and secuRe AI imaging based diaGnosis, stratification, fOllow-up, and preparedness for coronavirus paNdemics (DRAGON) |
| Amount | € 11,542,642 (EUR) |
| Funding ID | Grant Agreement (GA) No: 101005122 |
| Organisation | European Commission |
| Department | Innovative Medicines Initiative (IMI) |
| Sector | Public |
| Country | Belgium |
| Start | 10/2020 |
| End | 09/2023 |
| Title | A background correction method to compensate illumination variation in hyperspectral imaging |
| Description | Funder: FP7 People: Marie-Curie Actions; funder-id: http://dx.doi.org/10.13039/100011264; Grant(s): FP7-PEOPLE-2013-CIG-630729 |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| Impact | Not known |
| URL | https://www.repository.cam.ac.uk/handle/1810/303457 |
| Title | Dataset for: Characterizing optical fiber transmission matrices using metasurface reflector stacks for lensless imaging without distal access |
| Description | |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| URL | https://www.repository.cam.ac.uk/handle/1810/305512 |
| Title | Dataset for: First experience in clinical application of hyperspectral endoscopy for evaluation of colonic polyps |
| Description | All code and processed images used to prepare the figures associated with this manuscript. Raw endoscopy videos can be obtained on request from the corresponding author. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://www.repository.cam.ac.uk/handle/1810/323059 |
| Title | Dataset for: First-in-human pilot study of snapshot multispectral endoscopy for early detection of Barrett's-related neoplasia. |
| Description | Research data and code arising from a first-in-human endoscopy clinical trial (NCT03388047). Each of the data tables contains 9-point multispectral image spectra created from multispectral images captured using a snapshot multispectral endoscope. The raw images were processed into these image spectra using the code in 'PROCESSING CODE'. Raw image data can be provided upon request. The tissue spectra were captured in a first-in-human pilot study in the oesophagus. The colour spectra were captured from an X-rite colour target. Each row of the arrays contains the information about the spectra, most importantly, the labels from pathology. The different arrays contain spectra from averages over each multispectral image (processed_tissue_spectra.mat), each patient (processed_tissue_spectra_avg_per_trial.mat and processed_tissue_spectra_avg_per_trial_per_region_then_pooled.mat), and over all patients (processed_tissue_spectra_avg_overall.mat). Please see included readme files for further details. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://www.repository.cam.ac.uk/handle/1810/329675 |
| Title | Dataset for: Photoacoustic tomography detects response and resistance to Bevacizumab in breast cancer mouse models. |
| Description | Statistical analyses used to produce the figures in the associated manuscript. Detailed descriptions are contained within the R markdown and Prism files. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://www.repository.cam.ac.uk/handle/1810/334969 |
| Title | Dataset for: Quantitative phase and polarization imaging through an optical fiber applied to detection of early esophageal tumorigenesis |
| Description | |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| URL | https://www.repository.cam.ac.uk/handle/1810/304918 |
| Title | Dataset for: Spectral endoscopy combined with computational modelling and deep-learning yield high-contrast for oesophageal neoplasia |
| Description | Data upload contains data tables of attenuation and reflection spectra. Also contains the code used to generate the manuscript Figures and Tables from the data. Raw endoscopy videos can be obtained upon request from the corresponding author of the manuscript. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://www.repository.cam.ac.uk/handle/1810/323051 |
| Title | Deep learning applied to hyperspectral endoscopy for online spectral classification. |
| Description | Hyperspectral imaging (HSI) is being explored in endoscopy as a tool to extract biochemical information from tissue optical properties that may improve contrast for early cancer detection in the gastrointestinal tract. Motion artefacts during medical endoscopy have traditionally limited HSI application, however, recent developments in the field have led to real-time HSI deployments. Unfortunately, traditional HSI analysis methods remain unable to handle the volume of hyperspectral data in order to provide real-time feedback to the operator. Here, a convolutional neural network (CNN) is proposed to enable online classification of data obtained during HSI endoscopy. A five-layered CNN was trained and fine-tuned on a dataset of 300 hyperspectral endoscopy images acquired from a planar Macbeth ColorChecker chart and was able to distinguish between its 18 constituent colors with an average accuracy of 94.3% achieved at 8.8 fps. Performance was then tested on a set of images simulating an endoscopy environment, consisting of color charts warped inside a rigid tube mimicking a lumen. The algorithm improved robust to such variations, with classification accuracies over 90% being obtained despite the variations, with an average drop in accuracy of 2.4% being registered at the points of longest working distance and most inclination. For further validation of the color-based classification system, ex vivo videos of a methylene blue dyed pig esophagus and images of different cancer stages in the human esophagus were analyzed, showing spatially distinct color classifications. These results suggest that the CNN has potential to provide color-based classification during real-time HSI in endoscopy. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| Impact | Enquiries regarding use of the dataset |
| URL | https://www.repository.cam.ac.uk/handle/1810/302170 |
| Title | Hyperspectral Colour Classification (HCC) |
| Description | Dataset utilised for the training and testing of the spectral classification system for a line-scanning hyperspectral endoscope and the associated core algorithms. See the Readme file for a detailed description. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| Impact | None known |
| URL | https://www.repository.cam.ac.uk/handle/1810/303062 |
| Title | Research data supporting "A background correction method to compensate illumination variation in hyperspectral imaging" |
| Description | The dataset includes experimental and simulation data and the codes used in the paper "A background correction method to compensate illumination variation in hyperspectral imaging". All experimental data is captured using hyperspectral endoscopy (https://www.nature.com/articles/s41467-019-09484-4). See the Readme file for detailed information. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| Impact | Not known |
| URL | https://www.repository.cam.ac.uk/handle/1810/302032 |
| Title | Research data supporting "Deep learning as optimal control problems" |
| Description | |
| Type Of Material | Database/Collection of data |
| Year Produced | 2019 |
| Provided To Others? | Yes |
| URL | https://www.repository.cam.ac.uk/handle/1810/296197 |
| Title | Research data supporting 'An Anisotropic Interaction Model for Simulating Fingerprints' |
| Description | This data contains the code and data necessary to reproduce the computational results published in 'An Anisotropic Interaction Model for Simulating Fingerprints'. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2017 |
| Provided To Others? | Yes |
| Title | Research data supporting 'Pattern formation of a nonlocal, anisotropic interaction model' |
| Description | This data contains the code and data necessary to reproduce the computational results published in 'Pattern formation of a nonlocal, anisotropic interaction model'. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2017 |
| Provided To Others? | Yes |
| Title | Research data supporting the publication "Choose your path wisely: gradient descent in a Bregman distance framework" |
| Description | |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| URL | https://www.repository.cam.ac.uk/handle/1810/308472 |
| Title | Research data supporting the publication "Inverse Scale Space Decomposition". |
| Description | This dataset contains MATLAB© code for the numerical computation of the numerical examples described in Section 5.1 and Section 5.2 of the publication "Inverse Scale Space Decomposition". |
| Type Of Material | Database/Collection of data |
| Year Produced | 2017 |
| Provided To Others? | Yes |
| Title | Research data supporting the publication 'Nonlinear Spectral Image Fusion' |
| Description | This is Matlab code for the creation of image fusions based on the nonlinear spectral TV transform. The method of spectral image fusion is explained in the corresponding SSVM publication 'Nonlinear Spectral Image Fusion'. In order to run the automatic image fusion pipeline with the Obama/Reagan example as visualised in the paper, please follow the instructions in the readme.txt file in the folder 'spectralImageFusionOfFaces'. If you want to compute the spectral image fusions of Gauß and Newton in the supplementary files, please follow the instructions in the Matlab live scripts 'gaussnewton.mlx' or 'newtongauss.mlx' in the folder 'Banknote examples'. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2017 |
| Provided To Others? | Yes |
| Title | Robustness to misalignment of low-cost, compact quantitative phase imaging architectures |
| Description | Non-interferometric approaches to quantitative phase imaging could enable its application in low-cost, miniaturised settings such as capsule endoscopy. We present two possible architectures and both analyse and mitigate the effect of sensor misalignment on phase imaging performance. This is a crucial step towards determining the feasibility of implementing phase imaging in a capsule device. First, we investigate a design based on a folded 4f correlator, both in simulation and experimentally. We demonstrate a novel technique for identifying and compensating for axial misalignment and explore the limits of the approach. Next, we explore the implications of axial and transverse misalignment, and of manufacturing variations on the performance of a phase plate-based architecture, identifying a clear trade-off between phase plate resolution and algorithm convergence time. We conclude that while the phase plate architecture is more robust to misalignment, both architectures merit further development with the goal of realising a low-cost, compact system for applying phase imaging in capsule endoscopy. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| Impact | None known |
| URL | https://www.repository.cam.ac.uk/handle/1810/309619 |
| Description | Anisotropic variational models and PDEs for inverse imaging problems |
| Organisation | Luebeck University of Applied Sciences |
| Country | Germany |
| Sector | Academic/University |
| PI Contribution | In this project, we introduce a new higher-order total directional variation (TDV) regulariser for inverse imaging problems by taking into account the image gradient weighted by the structural content. Theoretical and numerical details are provided for different applications: the reconstruction of noisy images and videos, the image zooming and the interpolation of scattered surface data. The idea of using directional gradients for imaging applications is also used for the generalisation of the osmosis equation, introduced by Weickert and collaborators in 2013, to its anisotropic counter-part. Anisotropic osmosis is applied to the shadow removal problem thus improving upon the isotropic approach by avoiding the blurring artefact due to the isotropic diffusion. The main idea came from CB Schönlieb in Cambridge, and was part of Dr Simone Parisotto's PhD thesis (PhD student in Schönlieb's group). All the research work was done in collaboration with everyone involved. |
| Collaborator Contribution | Equal contribution between: Simon Masnou (Lyon), Jean-Michel Morel (Cachan), Jan Lellmann (Luebeck), Luca Calatroni (Ecole Polytechnique, Paris), and Joachim Weickert (Saarland). |
| Impact | [1] Parisotto S, Lellmann J, Masnou S, Schönlieb C-B. Higher-Order Total Directional Variation. Part I: Imaging Applications. ArXiv e-print (2018) https://arxiv.org/abs/1812.05023 [2] Parisotto S, Masnou S, Schönlieb C-B. Higher-Order Total Directional Variation. Part II: Analysis. ArXiv e-print (2018) https://arxiv.org/abs/1812.05061 [3] Parisotto S, Schönlieb C-B. Total Directional Variation for Video Denoising. ArXiv e-print (2018) https://arxiv.org/abs/1812.05063 [4] Parisotto S, Calatroni L, Caliari M, Schönlieb C-B, Weickert J. Anisotropic osmosis filtering for shadow removal in images. Inverse Problems (2019) https://doi.org/10.1088/1361-6420/ab08d2 |
| Start Year | 2014 |
| Description | Anisotropic variational models and PDEs for inverse imaging problems |
| Organisation | Saarland University |
| Country | Germany |
| Sector | Academic/University |
| PI Contribution | In this project, we introduce a new higher-order total directional variation (TDV) regulariser for inverse imaging problems by taking into account the image gradient weighted by the structural content. Theoretical and numerical details are provided for different applications: the reconstruction of noisy images and videos, the image zooming and the interpolation of scattered surface data. The idea of using directional gradients for imaging applications is also used for the generalisation of the osmosis equation, introduced by Weickert and collaborators in 2013, to its anisotropic counter-part. Anisotropic osmosis is applied to the shadow removal problem thus improving upon the isotropic approach by avoiding the blurring artefact due to the isotropic diffusion. The main idea came from CB Schönlieb in Cambridge, and was part of Dr Simone Parisotto's PhD thesis (PhD student in Schönlieb's group). All the research work was done in collaboration with everyone involved. |
| Collaborator Contribution | Equal contribution between: Simon Masnou (Lyon), Jean-Michel Morel (Cachan), Jan Lellmann (Luebeck), Luca Calatroni (Ecole Polytechnique, Paris), and Joachim Weickert (Saarland). |
| Impact | [1] Parisotto S, Lellmann J, Masnou S, Schönlieb C-B. Higher-Order Total Directional Variation. Part I: Imaging Applications. ArXiv e-print (2018) https://arxiv.org/abs/1812.05023 [2] Parisotto S, Masnou S, Schönlieb C-B. Higher-Order Total Directional Variation. Part II: Analysis. ArXiv e-print (2018) https://arxiv.org/abs/1812.05061 [3] Parisotto S, Schönlieb C-B. Total Directional Variation for Video Denoising. ArXiv e-print (2018) https://arxiv.org/abs/1812.05063 [4] Parisotto S, Calatroni L, Caliari M, Schönlieb C-B, Weickert J. Anisotropic osmosis filtering for shadow removal in images. Inverse Problems (2019) https://doi.org/10.1088/1361-6420/ab08d2 |
| Start Year | 2014 |
| Description | Anisotropic variational models and PDEs for inverse imaging problems |
| Organisation | University of Lyon |
| Country | France |
| Sector | Academic/University |
| PI Contribution | In this project, we introduce a new higher-order total directional variation (TDV) regulariser for inverse imaging problems by taking into account the image gradient weighted by the structural content. Theoretical and numerical details are provided for different applications: the reconstruction of noisy images and videos, the image zooming and the interpolation of scattered surface data. The idea of using directional gradients for imaging applications is also used for the generalisation of the osmosis equation, introduced by Weickert and collaborators in 2013, to its anisotropic counter-part. Anisotropic osmosis is applied to the shadow removal problem thus improving upon the isotropic approach by avoiding the blurring artefact due to the isotropic diffusion. The main idea came from CB Schönlieb in Cambridge, and was part of Dr Simone Parisotto's PhD thesis (PhD student in Schönlieb's group). All the research work was done in collaboration with everyone involved. |
| Collaborator Contribution | Equal contribution between: Simon Masnou (Lyon), Jean-Michel Morel (Cachan), Jan Lellmann (Luebeck), Luca Calatroni (Ecole Polytechnique, Paris), and Joachim Weickert (Saarland). |
| Impact | [1] Parisotto S, Lellmann J, Masnou S, Schönlieb C-B. Higher-Order Total Directional Variation. Part I: Imaging Applications. ArXiv e-print (2018) https://arxiv.org/abs/1812.05023 [2] Parisotto S, Masnou S, Schönlieb C-B. Higher-Order Total Directional Variation. Part II: Analysis. ArXiv e-print (2018) https://arxiv.org/abs/1812.05061 [3] Parisotto S, Schönlieb C-B. Total Directional Variation for Video Denoising. ArXiv e-print (2018) https://arxiv.org/abs/1812.05063 [4] Parisotto S, Calatroni L, Caliari M, Schönlieb C-B, Weickert J. Anisotropic osmosis filtering for shadow removal in images. Inverse Problems (2019) https://doi.org/10.1088/1361-6420/ab08d2 |
| Start Year | 2014 |
| Description | Artificial intelligence tools for screening mammography |
| Organisation | DeepMind Health |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Using my expertise as a breast screening radiologist to advise on development of machine reading tools for mammography. |
| Collaborator Contribution | DeepMInd developing machine reading tools for use in breast screening. These can then be tested by the partners in the NHS breast screening programme. |
| Impact | no outputs as yet... abstracts and publications expected. |
| Start Year | 2017 |
| Description | Cambridge big data |
| Organisation | Cambridge Carbon Capture Ltd |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | We have assisted in putting together research bids and a conference, as well as networking with others, and creating new collaborative projects |
| Collaborator Contribution | Assisted in putting together research bids and a conference, as well as networking with others, and creating new collaborative projects. The Big data consortium has provided huge expertise and knowledge for us. |
| Impact | A grant application, to enable better access and analysis of healthcare data has been submitted. A conference on big data in medicine is being planned for July 4th 2017. Academics have been put in contact with each other, enabling transfer of knowledge. |
| Start Year | 2016 |
| Description | Collaboration with Imperial College EPSRC maths centre |
| Organisation | Imperial College London |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | New collaboration to work on machine learning in medical images. Domain knowledge, imaging data, |
| Collaborator Contribution | New collaboration to work on machine learning in medical images. ML expertise. |
| Impact | None yet. |
| Start Year | 2018 |
| Description | Deep Multi-Task Models for Unsupervised MRI Registration |
| Organisation | Chinese University of Hong Kong |
| Country | Hong Kong |
| Sector | Academic/University |
| PI Contribution | Incorporating multiple tasks such as image registration into a given dynamic MRI reconstruction, it will result in a faster acquisition allowing for extreme under-sampling, and in higher time resolution leading to better image quality. That is- by sharing representation between related tasks, we can create synergies across complex tasks, which results in boosting the accuracy of our model whilst achieving better generalisation capabilities. This approach is known as Multi-task Learning (MTL) (pioneered by the work in [4]). In particular, we will explore unsupervised techniques for medical image registration, in which we will address the following questions: how to guarantee plausible transformations? What is the best strategy for avoiding using ground truth? |
| Collaborator Contribution | The project is exploring unsupervised techniques for medical image registration, in which we will address the project hypothesis |
| Impact | Too early for outcomes. |
| Start Year | 2019 |
| Description | Developing a Predictive Model Integrating Machine Learning and Image Inpainting to Delineate Tumour Invasion in Glioblastoma |
| Organisation | Fudan University |
| Department | Huashan Hospital |
| Country | China |
| Sector | Hospitals |
| PI Contribution | image processing of dataset, mathematical analysis of MRI, development of mathematical model |
| Collaborator Contribution | Fudan University: provision of expertise in neuroradiology Neurosciences (Cambridge): provision of data-set and annotations |
| Impact | Too early in the project for outcomes to be delivered |
| Start Year | 2018 |
| Description | Developing a Predictive Model Integrating Machine Learning and Image Inpainting to Delineate Tumour Invasion in Glioblastoma |
| Organisation | University of Cambridge |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | image processing of dataset, mathematical analysis of MRI, development of mathematical model |
| Collaborator Contribution | Fudan University: provision of expertise in neuroradiology Neurosciences (Cambridge): provision of data-set and annotations |
| Impact | Too early in the project for outcomes to be delivered |
| Start Year | 2018 |
| Description | Developing a multimodal imaging approach to characterize tumoral heterogeneity of advanced nasopharyngeal carcinoma |
| Organisation | Shanghai Jiao Tong University |
| Department | School of Medicine |
| Country | China |
| Sector | Academic/University |
| PI Contribution | Hosting the research project at the University of Cambridge and connection to relevant researchers within the CMIH group and Cambridge more generally. Provision of knowledge and expert input into the project |
| Collaborator Contribution | We propose this project to evaluate the role of MRI and PET/CT-based radiomics in the assessment between intratumoral heterogeneity and survival outcomes and guiding individual radiation plan and chemotherapy for patients with LA-NPC. MRI and PET/CT-based habitat imaging and radiomics could characterize tumor heterogeneity and have clinical significance in patients with LA-NPC. |
| Impact | To early to report |
| Start Year | 2019 |
| Description | Developing a multimodal imaging approach to characterize tumoral heterogeneity of advanced nasopharyngeal carcinoma |
| Organisation | University of Cambridge |
| Department | Department of Pure Mathematics and Mathematical Statistics |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Hosting the research project at the University of Cambridge and connection to relevant researchers within the CMIH group and Cambridge more generally. Provision of knowledge and expert input into the project |
| Collaborator Contribution | We propose this project to evaluate the role of MRI and PET/CT-based radiomics in the assessment between intratumoral heterogeneity and survival outcomes and guiding individual radiation plan and chemotherapy for patients with LA-NPC. MRI and PET/CT-based habitat imaging and radiomics could characterize tumor heterogeneity and have clinical significance in patients with LA-NPC. |
| Impact | To early to report |
| Start Year | 2019 |
| Description | Duke - MRI microscopy |
| Organisation | Duke University |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | Provide expertise on image reconstruction from undersampled data. |
| Collaborator Contribution | Partern is applied their expertise in high resolution MRI and microscopic scale to improve reconstruction quality. |
| Impact | Journal submission is underway. |
| Start Year | 2016 |
| Description | Expectation-maximization regularized Deep Learning (EMRDL) for semi-supervised segmentation of glioblastoma using multi-modal MR images |
| Organisation | Peking University |
| Country | China |
| Sector | Academic/University |
| PI Contribution | We will develop a semi-supervised machine learning framework to segment the glioblastoma from multiparametric MRI. The model needs: • To be trained on partial voxel-wise annotations, i.e., only the high-confidence tumor and nontumor region is labeled by experienced physicians. • To effectively integrate the clinical prior knowledge regarding the infiltration features in multiparametric MRI. • To predict the tumor boundaries from the non-labeled multiparametric MRI, including the boundaries of high-confidence tumor region and non-tumor regions, and the inferred boundaries. |
| Collaborator Contribution | We will formulate an EM-based framework to integrate the prior knowledge into the DL model, which automatically learns the image features of high-confidence regions and further infers the pixel labels in nonconfident regions, by combing the strength of DL and statistical inference. |
| Impact | Too early to report |
| Start Year | 2020 |
| Description | Faster PET Reconstruction by Stochastic Optimisation |
| Organisation | Ecole Polytechnique |
| Country | France |
| Sector | Academic/University |
| PI Contribution | This project is concerned with the efficient reconstruction of positron emission tomography by means of stochastic optimisation. In the last decade, many mathematical tools have been developed that have the ability to enhance clinical imaging in various ways. On the forefront of this wave are non-smooth priors that allow the reconstruction of a smooth image but do not prohibit jumps across meaningful areas like organs in medical imaging. Beside this these new tools also allow the incorporation of a-prior structual knowledge about the solution at hand. However, most of this progress has not been translated into clinical practice as most modern algorithms are too demanding for the huge data sizes encountered. In the past, algorithms have been made "applicable" to clinical practices by only considering a subset if the data at a time. While for some models this leads to satisfactory results, in general this ad-hoc strategy may yield to spurious artefacts. Motivated by the success of similar techniques in machine learning, in this project we extend modern algorithms for imaging that can handle non-smooth priors in a rigorous way to the subset setting by means of "randomisation". While the algorithm and thus its iterates are random, the variances of these are low and converge quickly to the desired deterministic solution. The Cambridge group has contributed to the algorithm development, and the design of the PET++ project (see outputs below). |
| Collaborator Contribution | Matthias Ehrhardt at the University of Bath has lead the algorithm development for stochastic optimisation for PET, and is a co-Lead on the PET++ project. Antonin Chambolle (Ecole Polytechnique) and Peter Richtárik (KAUST) have contributed with their expertise in convex optimisation and subspace decomposition approaches. Pawel Markiewicz (UCL) has contributed with his expertise on PET and associated PET data and reconstruction codes. |
| Impact | [1] Ehrhardt, M. J., Markiewicz, P. J., Schönlieb, C.-B. (2018). Faster PET Reconstruction with Non-Smooth Priors by Randomization and Preconditioning, https://arxiv.org/abs/1808.07150 [2] Ehrhardt, M. J., Markiewicz, P. J., Richtárik, P., Schott, J., Chambolle, A. & Schönlieb, C.-B. (2017). Faster PET Reconstruction with a Stochastic Primal-Dual Hybrid Gradient Method. In Proceedings of SPIE (Vol. 10394, pp. 1-12). San Diego. http://doi.org/10.1117/12.2272946. [3] Chambolle, A., Ehrhardt, M. J., Richtárik, P., & Schönlieb, C.-B. (2017). Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications. to appear in SIAM Journal on Optimization. http://arxiv.org/abs/1706.04957. |
| Start Year | 2016 |
| Description | Faster PET Reconstruction by Stochastic Optimisation |
| Organisation | GE Healthcare Limited |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | This project is concerned with the efficient reconstruction of positron emission tomography by means of stochastic optimisation. In the last decade, many mathematical tools have been developed that have the ability to enhance clinical imaging in various ways. On the forefront of this wave are non-smooth priors that allow the reconstruction of a smooth image but do not prohibit jumps across meaningful areas like organs in medical imaging. Beside this these new tools also allow the incorporation of a-prior structual knowledge about the solution at hand. However, most of this progress has not been translated into clinical practice as most modern algorithms are too demanding for the huge data sizes encountered. In the past, algorithms have been made "applicable" to clinical practices by only considering a subset if the data at a time. While for some models this leads to satisfactory results, in general this ad-hoc strategy may yield to spurious artefacts. Motivated by the success of similar techniques in machine learning, in this project we extend modern algorithms for imaging that can handle non-smooth priors in a rigorous way to the subset setting by means of "randomisation". While the algorithm and thus its iterates are random, the variances of these are low and converge quickly to the desired deterministic solution. The Cambridge group has contributed to the algorithm development, and the design of the PET++ project (see outputs below). |
| Collaborator Contribution | Matthias Ehrhardt at the University of Bath has lead the algorithm development for stochastic optimisation for PET, and is a co-Lead on the PET++ project. Antonin Chambolle (Ecole Polytechnique) and Peter Richtárik (KAUST) have contributed with their expertise in convex optimisation and subspace decomposition approaches. Pawel Markiewicz (UCL) has contributed with his expertise on PET and associated PET data and reconstruction codes. |
| Impact | [1] Ehrhardt, M. J., Markiewicz, P. J., Schönlieb, C.-B. (2018). Faster PET Reconstruction with Non-Smooth Priors by Randomization and Preconditioning, https://arxiv.org/abs/1808.07150 [2] Ehrhardt, M. J., Markiewicz, P. J., Richtárik, P., Schott, J., Chambolle, A. & Schönlieb, C.-B. (2017). Faster PET Reconstruction with a Stochastic Primal-Dual Hybrid Gradient Method. In Proceedings of SPIE (Vol. 10394, pp. 1-12). San Diego. http://doi.org/10.1117/12.2272946. [3] Chambolle, A., Ehrhardt, M. J., Richtárik, P., & Schönlieb, C.-B. (2017). Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications. to appear in SIAM Journal on Optimization. http://arxiv.org/abs/1706.04957. |
| Start Year | 2016 |
| Description | Faster PET Reconstruction by Stochastic Optimisation |
| Organisation | King Abdullah University of Science and Technology (KAUST) |
| Country | Saudi Arabia |
| Sector | Academic/University |
| PI Contribution | This project is concerned with the efficient reconstruction of positron emission tomography by means of stochastic optimisation. In the last decade, many mathematical tools have been developed that have the ability to enhance clinical imaging in various ways. On the forefront of this wave are non-smooth priors that allow the reconstruction of a smooth image but do not prohibit jumps across meaningful areas like organs in medical imaging. Beside this these new tools also allow the incorporation of a-prior structual knowledge about the solution at hand. However, most of this progress has not been translated into clinical practice as most modern algorithms are too demanding for the huge data sizes encountered. In the past, algorithms have been made "applicable" to clinical practices by only considering a subset if the data at a time. While for some models this leads to satisfactory results, in general this ad-hoc strategy may yield to spurious artefacts. Motivated by the success of similar techniques in machine learning, in this project we extend modern algorithms for imaging that can handle non-smooth priors in a rigorous way to the subset setting by means of "randomisation". While the algorithm and thus its iterates are random, the variances of these are low and converge quickly to the desired deterministic solution. The Cambridge group has contributed to the algorithm development, and the design of the PET++ project (see outputs below). |
| Collaborator Contribution | Matthias Ehrhardt at the University of Bath has lead the algorithm development for stochastic optimisation for PET, and is a co-Lead on the PET++ project. Antonin Chambolle (Ecole Polytechnique) and Peter Richtárik (KAUST) have contributed with their expertise in convex optimisation and subspace decomposition approaches. Pawel Markiewicz (UCL) has contributed with his expertise on PET and associated PET data and reconstruction codes. |
| Impact | [1] Ehrhardt, M. J., Markiewicz, P. J., Schönlieb, C.-B. (2018). Faster PET Reconstruction with Non-Smooth Priors by Randomization and Preconditioning, https://arxiv.org/abs/1808.07150 [2] Ehrhardt, M. J., Markiewicz, P. J., Richtárik, P., Schott, J., Chambolle, A. & Schönlieb, C.-B. (2017). Faster PET Reconstruction with a Stochastic Primal-Dual Hybrid Gradient Method. In Proceedings of SPIE (Vol. 10394, pp. 1-12). San Diego. http://doi.org/10.1117/12.2272946. [3] Chambolle, A., Ehrhardt, M. J., Richtárik, P., & Schönlieb, C.-B. (2017). Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications. to appear in SIAM Journal on Optimization. http://arxiv.org/abs/1706.04957. |
| Start Year | 2016 |
| Description | Faster PET Reconstruction by Stochastic Optimisation |
| Organisation | University College London |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | This project is concerned with the efficient reconstruction of positron emission tomography by means of stochastic optimisation. In the last decade, many mathematical tools have been developed that have the ability to enhance clinical imaging in various ways. On the forefront of this wave are non-smooth priors that allow the reconstruction of a smooth image but do not prohibit jumps across meaningful areas like organs in medical imaging. Beside this these new tools also allow the incorporation of a-prior structual knowledge about the solution at hand. However, most of this progress has not been translated into clinical practice as most modern algorithms are too demanding for the huge data sizes encountered. In the past, algorithms have been made "applicable" to clinical practices by only considering a subset if the data at a time. While for some models this leads to satisfactory results, in general this ad-hoc strategy may yield to spurious artefacts. Motivated by the success of similar techniques in machine learning, in this project we extend modern algorithms for imaging that can handle non-smooth priors in a rigorous way to the subset setting by means of "randomisation". While the algorithm and thus its iterates are random, the variances of these are low and converge quickly to the desired deterministic solution. The Cambridge group has contributed to the algorithm development, and the design of the PET++ project (see outputs below). |
| Collaborator Contribution | Matthias Ehrhardt at the University of Bath has lead the algorithm development for stochastic optimisation for PET, and is a co-Lead on the PET++ project. Antonin Chambolle (Ecole Polytechnique) and Peter Richtárik (KAUST) have contributed with their expertise in convex optimisation and subspace decomposition approaches. Pawel Markiewicz (UCL) has contributed with his expertise on PET and associated PET data and reconstruction codes. |
| Impact | [1] Ehrhardt, M. J., Markiewicz, P. J., Schönlieb, C.-B. (2018). Faster PET Reconstruction with Non-Smooth Priors by Randomization and Preconditioning, https://arxiv.org/abs/1808.07150 [2] Ehrhardt, M. J., Markiewicz, P. J., Richtárik, P., Schott, J., Chambolle, A. & Schönlieb, C.-B. (2017). Faster PET Reconstruction with a Stochastic Primal-Dual Hybrid Gradient Method. In Proceedings of SPIE (Vol. 10394, pp. 1-12). San Diego. http://doi.org/10.1117/12.2272946. [3] Chambolle, A., Ehrhardt, M. J., Richtárik, P., & Schönlieb, C.-B. (2017). Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications. to appear in SIAM Journal on Optimization. http://arxiv.org/abs/1706.04957. |
| Start Year | 2016 |
| Description | Faster PET Reconstruction by Stochastic Optimisation |
| Organisation | University of Bath |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | This project is concerned with the efficient reconstruction of positron emission tomography by means of stochastic optimisation. In the last decade, many mathematical tools have been developed that have the ability to enhance clinical imaging in various ways. On the forefront of this wave are non-smooth priors that allow the reconstruction of a smooth image but do not prohibit jumps across meaningful areas like organs in medical imaging. Beside this these new tools also allow the incorporation of a-prior structual knowledge about the solution at hand. However, most of this progress has not been translated into clinical practice as most modern algorithms are too demanding for the huge data sizes encountered. In the past, algorithms have been made "applicable" to clinical practices by only considering a subset if the data at a time. While for some models this leads to satisfactory results, in general this ad-hoc strategy may yield to spurious artefacts. Motivated by the success of similar techniques in machine learning, in this project we extend modern algorithms for imaging that can handle non-smooth priors in a rigorous way to the subset setting by means of "randomisation". While the algorithm and thus its iterates are random, the variances of these are low and converge quickly to the desired deterministic solution. The Cambridge group has contributed to the algorithm development, and the design of the PET++ project (see outputs below). |
| Collaborator Contribution | Matthias Ehrhardt at the University of Bath has lead the algorithm development for stochastic optimisation for PET, and is a co-Lead on the PET++ project. Antonin Chambolle (Ecole Polytechnique) and Peter Richtárik (KAUST) have contributed with their expertise in convex optimisation and subspace decomposition approaches. Pawel Markiewicz (UCL) has contributed with his expertise on PET and associated PET data and reconstruction codes. |
| Impact | [1] Ehrhardt, M. J., Markiewicz, P. J., Schönlieb, C.-B. (2018). Faster PET Reconstruction with Non-Smooth Priors by Randomization and Preconditioning, https://arxiv.org/abs/1808.07150 [2] Ehrhardt, M. J., Markiewicz, P. J., Richtárik, P., Schott, J., Chambolle, A. & Schönlieb, C.-B. (2017). Faster PET Reconstruction with a Stochastic Primal-Dual Hybrid Gradient Method. In Proceedings of SPIE (Vol. 10394, pp. 1-12). San Diego. http://doi.org/10.1117/12.2272946. [3] Chambolle, A., Ehrhardt, M. J., Richtárik, P., & Schönlieb, C.-B. (2017). Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications. to appear in SIAM Journal on Optimization. http://arxiv.org/abs/1706.04957. |
| Start Year | 2016 |
| Description | Flow of Microtubules in the Drosophila Oocyte |
| Organisation | CLK GmBH |
| Country | Germany |
| Sector | Private |
| PI Contribution | The focus of this project is to characterise directionality of plus ends of microtubules in confocal microscopy images of Drosophilia embryos. This goal is particularly challenging due to the high noise level in such data, making it almost impossible to distinguish EB1 fluorescently labelled comets from randomly distributed noise. To overcome this problem, we employ recently developed methods for joint motion estimation and image reconstruction. As a result we are able to estimate motion in image sequences where other state of the art methods fail. My group in Cambridge (in particular my PostDoc Dr Lukas Lang) has contributed the algorithm development for the motion analysis (optical flow). |
| Collaborator Contribution | Our collaborators Isabel Palacios (Queen Mary London) and Mail Drechsler (University of Osnabrück) have contributed with the biological question and the imaging data. Our collaborators Martin Burger (University of Erlangen) and Hendrik Dirks (CLK GmbH) have contributed to initial ideas for the motion estimation algorithm. |
| Impact | M. Drechsler, L. F. Lang, H. Dirks, M. Burger, C.-B. Schönlieb, I. M. Palacios. Optical flow analysis reveals that Kinesin-mediated advection impacts on the orientation of microtubules, submitted, 2019. Preprint: https://www.biorxiv.org/content/10.1101/556043v2 Code: https://zenodo.org/record/2573254#.XIduWS2cZ0s |
| Start Year | 2015 |
| Description | Flow of Microtubules in the Drosophila Oocyte |
| Organisation | Friedrich-Alexander University Erlangen-Nuremberg |
| Country | Germany |
| Sector | Academic/University |
| PI Contribution | The focus of this project is to characterise directionality of plus ends of microtubules in confocal microscopy images of Drosophilia embryos. This goal is particularly challenging due to the high noise level in such data, making it almost impossible to distinguish EB1 fluorescently labelled comets from randomly distributed noise. To overcome this problem, we employ recently developed methods for joint motion estimation and image reconstruction. As a result we are able to estimate motion in image sequences where other state of the art methods fail. My group in Cambridge (in particular my PostDoc Dr Lukas Lang) has contributed the algorithm development for the motion analysis (optical flow). |
| Collaborator Contribution | Our collaborators Isabel Palacios (Queen Mary London) and Mail Drechsler (University of Osnabrück) have contributed with the biological question and the imaging data. Our collaborators Martin Burger (University of Erlangen) and Hendrik Dirks (CLK GmbH) have contributed to initial ideas for the motion estimation algorithm. |
| Impact | M. Drechsler, L. F. Lang, H. Dirks, M. Burger, C.-B. Schönlieb, I. M. Palacios. Optical flow analysis reveals that Kinesin-mediated advection impacts on the orientation of microtubules, submitted, 2019. Preprint: https://www.biorxiv.org/content/10.1101/556043v2 Code: https://zenodo.org/record/2573254#.XIduWS2cZ0s |
| Start Year | 2015 |
| Description | Flow of Microtubules in the Drosophila Oocyte |
| Organisation | Queen Mary University of London |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | The focus of this project is to characterise directionality of plus ends of microtubules in confocal microscopy images of Drosophilia embryos. This goal is particularly challenging due to the high noise level in such data, making it almost impossible to distinguish EB1 fluorescently labelled comets from randomly distributed noise. To overcome this problem, we employ recently developed methods for joint motion estimation and image reconstruction. As a result we are able to estimate motion in image sequences where other state of the art methods fail. My group in Cambridge (in particular my PostDoc Dr Lukas Lang) has contributed the algorithm development for the motion analysis (optical flow). |
| Collaborator Contribution | Our collaborators Isabel Palacios (Queen Mary London) and Mail Drechsler (University of Osnabrück) have contributed with the biological question and the imaging data. Our collaborators Martin Burger (University of Erlangen) and Hendrik Dirks (CLK GmbH) have contributed to initial ideas for the motion estimation algorithm. |
| Impact | M. Drechsler, L. F. Lang, H. Dirks, M. Burger, C.-B. Schönlieb, I. M. Palacios. Optical flow analysis reveals that Kinesin-mediated advection impacts on the orientation of microtubules, submitted, 2019. Preprint: https://www.biorxiv.org/content/10.1101/556043v2 Code: https://zenodo.org/record/2573254#.XIduWS2cZ0s |
| Start Year | 2015 |
| Description | Flow of Microtubules in the Drosophila Oocyte |
| Organisation | University of Osnabrück |
| Country | Germany |
| Sector | Academic/University |
| PI Contribution | The focus of this project is to characterise directionality of plus ends of microtubules in confocal microscopy images of Drosophilia embryos. This goal is particularly challenging due to the high noise level in such data, making it almost impossible to distinguish EB1 fluorescently labelled comets from randomly distributed noise. To overcome this problem, we employ recently developed methods for joint motion estimation and image reconstruction. As a result we are able to estimate motion in image sequences where other state of the art methods fail. My group in Cambridge (in particular my PostDoc Dr Lukas Lang) has contributed the algorithm development for the motion analysis (optical flow). |
| Collaborator Contribution | Our collaborators Isabel Palacios (Queen Mary London) and Mail Drechsler (University of Osnabrück) have contributed with the biological question and the imaging data. Our collaborators Martin Burger (University of Erlangen) and Hendrik Dirks (CLK GmbH) have contributed to initial ideas for the motion estimation algorithm. |
| Impact | M. Drechsler, L. F. Lang, H. Dirks, M. Burger, C.-B. Schönlieb, I. M. Palacios. Optical flow analysis reveals that Kinesin-mediated advection impacts on the orientation of microtubules, submitted, 2019. Preprint: https://www.biorxiv.org/content/10.1101/556043v2 Code: https://zenodo.org/record/2573254#.XIduWS2cZ0s |
| Start Year | 2015 |
| Description | Geometric Integration Methods for Optimisation |
| Organisation | La Trobe University |
| Country | Australia |
| Sector | Academic/University |
| PI Contribution | This project is concerned with the development and analysis of optimisation schemes based on geometric numerical integration methods. Discrete gradient methods are popular numerical schemes for solving systems of ODEs, and are known for preserving structures of the continuous system such as energy dissipation/conservation. Applying discrete gradients to dissipative ODEs/PDEs yields optimisation schemes that preserve the dissipative structure. For example, we consider a derivative-free discrete gradient method for optimising nonsmooth, nonconvex problems in a blackbox setting. This method has been shown to converge to optimal points of the objective function in a general, nonsmooth setting, while retaining favourable properties of gradient flow. This blackbox optimisation framework is useful, for instance, for bilevel optimisation of regularisation parameters in image processing. We (my PostDoc Matthias Ehrhardt and myself) have developed the idea and this topic in Cambridge, and my PhD student Erlend Riis has done all the analysis and numerical tests that are contained in thee papers (see below). |
| Collaborator Contribution | Our collaborator Reinout Quispel (La Trobe, Melbourne, Australia) has contributed expertise in geometric integration. Our collaborator Torbjørn Ringholm (NTNU, Trondheim, Norway) has contributed to the convergence analysis in the smooth case, and was the lead author on the Euler elastica paper. Our collaborator Jasmina Lazic (former MathWorks Cambridge) has contributed to the parallelisation of the discrete gradient method. |
| Impact | E. S. Riis, M. J. Ehrhardt, G. R. W. Quispel, and C.-B. Schönlieb, A geometric integration approach to nonsmooth, nonconvex optimisation, arXiv:1807.07554, 2018. M. Ehrhardt, E. Riis, T. Ringholm, and C.-B. Schönlieb, A geometric integration approach to smooth optimisation: Foundations of the discrete gradient method, arXiv:1805.06444, 2018. T. Ringholm, J. Lazic, C.-B. Schönlieb, Variational image regularization with Euler's elastica using a discrete gradient scheme, SIAM J. Imaging Sci., 11(4), 2665-2691, 2018. Exhibition at MATLAB Expo in Silverstone in 2017. |
| Start Year | 2017 |
| Description | Geometric Integration Methods for Optimisation |
| Organisation | Norwegian University of Science and Technology (NTNU) |
| Country | Norway |
| Sector | Academic/University |
| PI Contribution | This project is concerned with the development and analysis of optimisation schemes based on geometric numerical integration methods. Discrete gradient methods are popular numerical schemes for solving systems of ODEs, and are known for preserving structures of the continuous system such as energy dissipation/conservation. Applying discrete gradients to dissipative ODEs/PDEs yields optimisation schemes that preserve the dissipative structure. For example, we consider a derivative-free discrete gradient method for optimising nonsmooth, nonconvex problems in a blackbox setting. This method has been shown to converge to optimal points of the objective function in a general, nonsmooth setting, while retaining favourable properties of gradient flow. This blackbox optimisation framework is useful, for instance, for bilevel optimisation of regularisation parameters in image processing. We (my PostDoc Matthias Ehrhardt and myself) have developed the idea and this topic in Cambridge, and my PhD student Erlend Riis has done all the analysis and numerical tests that are contained in thee papers (see below). |
| Collaborator Contribution | Our collaborator Reinout Quispel (La Trobe, Melbourne, Australia) has contributed expertise in geometric integration. Our collaborator Torbjørn Ringholm (NTNU, Trondheim, Norway) has contributed to the convergence analysis in the smooth case, and was the lead author on the Euler elastica paper. Our collaborator Jasmina Lazic (former MathWorks Cambridge) has contributed to the parallelisation of the discrete gradient method. |
| Impact | E. S. Riis, M. J. Ehrhardt, G. R. W. Quispel, and C.-B. Schönlieb, A geometric integration approach to nonsmooth, nonconvex optimisation, arXiv:1807.07554, 2018. M. Ehrhardt, E. Riis, T. Ringholm, and C.-B. Schönlieb, A geometric integration approach to smooth optimisation: Foundations of the discrete gradient method, arXiv:1805.06444, 2018. T. Ringholm, J. Lazic, C.-B. Schönlieb, Variational image regularization with Euler's elastica using a discrete gradient scheme, SIAM J. Imaging Sci., 11(4), 2665-2691, 2018. Exhibition at MATLAB Expo in Silverstone in 2017. |
| Start Year | 2017 |
| Description | Helium Microscopy |
| Organisation | Duke University |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | Developing advanced image reconstruction and sampling techniques for imaging nanosurfaces with helium atoms |
| Collaborator Contribution | Cavendish has been building a microscope prototype for imaging nanosurfaces with helium atoms. Duke has partnered on the reconstruction and sampling aspects. |
| Impact | Multi-disciplinary collaboration bringing together hard physical sciences and mathematics. The work will produce the world's first scanning helium microscope and hope to establish it as a cutting edge research tool in industrial applications. Part of the work is to design acquisition and reconstruction techniques that can enable the technique to be more robust and/or improve its spatial resolution. |
| Start Year | 2018 |
| Description | Helium Microscopy |
| Organisation | University of Cambridge |
| Department | Cavendish Laboratory |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Developing advanced image reconstruction and sampling techniques for imaging nanosurfaces with helium atoms |
| Collaborator Contribution | Cavendish has been building a microscope prototype for imaging nanosurfaces with helium atoms. Duke has partnered on the reconstruction and sampling aspects. |
| Impact | Multi-disciplinary collaboration bringing together hard physical sciences and mathematics. The work will produce the world's first scanning helium microscope and hope to establish it as a cutting edge research tool in industrial applications. Part of the work is to design acquisition and reconstruction techniques that can enable the technique to be more robust and/or improve its spatial resolution. |
| Start Year | 2018 |
| Description | Integrative Cancer Medicine Collaboration |
| Organisation | Cancer Research UK Cambridge Institute |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Development of all-in-one cancer imaging pipeline: from raw tomographic measurements to personalised cancer diagnosis and treatment prediction. |
| Collaborator Contribution | Integrative cancer medicine idea; provision of problem and objective; provision of clinical data and expertise. Collaborators: Prof. Evis Sala (Radiology, Cambridge), Prof. Ozan Öktem (KTH, Stockholm), Dr Mireia Crispin-Ortuzar (CRUK CI), Dr Ramona Woitek (Radiology, Cambridge) |
| Impact | Wellcome Trust Application for All-in-one cancer imaging project under the Digital Innovator Award call. Outcome to be known in April 2019. |
| Start Year | 2018 |
| Description | Integrative Cancer Medicine Collaboration |
| Organisation | Royal Institute of Technology |
| Country | Sweden |
| Sector | Academic/University |
| PI Contribution | Development of all-in-one cancer imaging pipeline: from raw tomographic measurements to personalised cancer diagnosis and treatment prediction. |
| Collaborator Contribution | Integrative cancer medicine idea; provision of problem and objective; provision of clinical data and expertise. Collaborators: Prof. Evis Sala (Radiology, Cambridge), Prof. Ozan Öktem (KTH, Stockholm), Dr Mireia Crispin-Ortuzar (CRUK CI), Dr Ramona Woitek (Radiology, Cambridge) |
| Impact | Wellcome Trust Application for All-in-one cancer imaging project under the Digital Innovator Award call. Outcome to be known in April 2019. |
| Start Year | 2018 |
| Description | Johnson & Johnson |
| Organisation | Johnson & Johnson |
| Country | United States |
| Sector | Private |
| PI Contribution | We are bringing IP in optical filter fabrication to enable multispectral imaging using OVT sensors by direct deposition of filters using electron beam lithography and photolithography |
| Collaborator Contribution | J&J (Auris) are providing bare silicon wafers for us to test our filter technologies for direct deposition onto CMOS image sensors |
| Impact | Multidisciplinary, engineering, physics, medical. No outputs as yet |
| Start Year | 2019 |
| Description | Joint Variational Reconstruction and Motion Estimation via Sparse Dictionary Learning for Robust Optical |
| Organisation | National Institute of Applied Sciences of Rouen |
| Country | France |
| Sector | Academic/University |
| PI Contribution | A major challenge in MRI is - how to reduce the data-acquisition time whilst generating high-quality images? In this project, we seek to exploit the strong repercussion of motion during the image formation by developing a single model that links, simultaneously and explicitly, the MRI reconstruction and the physical motion estimation. |
| Collaborator Contribution | Our collaborators have contributed with expert knowledge in their respective fields and facilitated access to imaging data |
| Impact | Corona, V., Aviles-Rivero, A. I., Debroux, N., Le Guyader, C., & Schönlieb, C. B. (2019). Variational Multi-Task MRI Reconstruction: Joint Reconstruction, Registration and Super-Resolution. arXiv preprint arXiv:1908.05911. |
| Start Year | 2019 |
| Description | Joint Variational Reconstruction and Motion Estimation via Sparse Dictionary Learning for Robust Optical |
| Organisation | University of Cambridge |
| Department | Department of Applied Mathematics and Theoretical Physics (DAMTP) |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | A major challenge in MRI is - how to reduce the data-acquisition time whilst generating high-quality images? In this project, we seek to exploit the strong repercussion of motion during the image formation by developing a single model that links, simultaneously and explicitly, the MRI reconstruction and the physical motion estimation. |
| Collaborator Contribution | Our collaborators have contributed with expert knowledge in their respective fields and facilitated access to imaging data |
| Impact | Corona, V., Aviles-Rivero, A. I., Debroux, N., Le Guyader, C., & Schönlieb, C. B. (2019). Variational Multi-Task MRI Reconstruction: Joint Reconstruction, Registration and Super-Resolution. arXiv preprint arXiv:1908.05911. |
| Start Year | 2019 |
| Description | Joint Variational Reconstruction and Motion Estimation via Sparse Dictionary Learning for Robust Optical |
| Organisation | University of Cambridge |
| Department | Department of Pure Mathematics and Mathematical Statistics |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | A major challenge in MRI is - how to reduce the data-acquisition time whilst generating high-quality images? In this project, we seek to exploit the strong repercussion of motion during the image formation by developing a single model that links, simultaneously and explicitly, the MRI reconstruction and the physical motion estimation. |
| Collaborator Contribution | Our collaborators have contributed with expert knowledge in their respective fields and facilitated access to imaging data |
| Impact | Corona, V., Aviles-Rivero, A. I., Debroux, N., Le Guyader, C., & Schönlieb, C. B. (2019). Variational Multi-Task MRI Reconstruction: Joint Reconstruction, Registration and Super-Resolution. arXiv preprint arXiv:1908.05911. |
| Start Year | 2019 |
| Description | MSSM |
| Organisation | Icahn School of Medicine at Mount Sinai |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | Long collaboration with Prof Zahi Fayad. |
| Collaborator Contribution | Exchange of students between Cambridge, Edinburgh and Mount Sinai. |
| Impact | Many papers and joint grant applications. |
| Start Year | 2008 |
| Description | Mathematical Challenges in Electron Tomography |
| Organisation | University of Manchester |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Electron microscopy is a powerful tool in the physical, biological, and industrial sciences advancing areas from nanotechnology to drug discovery. Tomography is a mathematical technique used to recover full 3D information from a sequence of 2D images. One of the classical challenges here is to get the best quality reconstruction from the smallest amount of data. Some of our work has been to introduce novel and customised regularisation techniques to address such problems. Recent hardware advances have also extended this to spectral images, where pixel-wise values of the 2D images are vectors rather than greyscale. This data is also very slow to acquire so we need new methods to reconstruct from little and very noisy data. I have a joint PhD student, Mr Robert Tovey, with Paul Midgley in Material Sciences in Cambridge. Together with Rob my group has contributed the development of novel mathematical reconstruction algorithms for electron tomography reconstruction. Our latest project is the derivation of a sound mathematical model and associated numerical algorithm for strain tensor tomography (in collaboration with Bill Lionheart from Manchester). |
| Collaborator Contribution | Our collaborators in the group of Paul Midgley in Cambridge are the problem owners and have contributed with their expert knowledge on electron tomography and associated tomographic imaging data. Our collaborator Bill Lionheart from Manchester has contributed with his expertise on tensor tomography. |
| Impact | Liquid phase blending of metal-organic frameworks L Longley, SM Collins, C Zhou, GJ Smales, SE Norman, NJ Brownbill, ... Nature communications 9 (1), 2135 Entropic comparison of atomic-resolution electron tomography of crystals and amorphous materials SM Collins, RK Leary, PA Midgley, R Tovey, M Benning, CB Schönlieb, ... Physical review letters 119 (16), 166101 Directional sinogram inpainting for limited angle tomography R Tovey, M Benning, C Brune, MJ Lagerwerf, SM Collins, R Leary, ... Inverse Problems Automated Textural Classification of Osteoarthritis Magnetic Resonance Images JD Kaggie, R Tovey, JW MacKay, F Gilbert, FA Gallagher, A McCaskie, ... International Society for Magnetic Resonance in Medicine |
| Start Year | 2017 |
| Description | Mechanical modelling of coronary atherosclerosis |
| Organisation | Monash University |
| Country | Australia |
| Sector | Academic/University |
| PI Contribution | Image processing, numerical modelling, statistical analysis and results interpretation. |
| Collaborator Contribution | Study design, data collection and results interpretation. |
| Impact | Not yet. |
| Start Year | 2017 |
| Description | Novel techniques of tumour detection in PET imaging |
| Organisation | University of Glasgow |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Establish the collaboration with the researcher and to create connections with members of the CMIH in order to develop the project aims. Provision of expertise relevant to building the research framework. |
| Collaborator Contribution | The primary goal of this research is to build a statistical/mathematical framework for automated PET image analysis that is closer to human perception. Although manual interpretation of the PET image is more accurate and reproducible than thresholding-based semiautomatic segmentation methods, human contouring has large interobserver and intraobserver variations and moreover, it is extremely time-consuming. Further, it is harder for humans to analyze more than two dimensions at a time and it becomes even harder if multiple modalities are involved. Moreover, if the task is to analyze a series of images it quickly becomes an onerous job for a single human. |
| Impact | Closer ties between research institutions and very high likelihood of a combined grant application in the near future. |
| Start Year | 2019 |
| Description | PreXion - LED photoacoustics |
| Organisation | PreXion Corporation |
| Country | Japan |
| Sector | Private |
| PI Contribution | We will apply our technical instrument validation methods to the Prexion imaging system |
| Collaborator Contribution | They have provided funding for a postdoc for 6 months to carry out the research and also for the installation of an imaging system to test. |
| Impact | Not yet. |
| Start Year | 2016 |
| Description | Proximal-Nets: Unfolding Proximal Algorithms for Accelerating and Improving MR Image Reconstruction |
| Organisation | Beijing Institute of Technology |
| Country | China |
| Sector | Academic/University |
| PI Contribution | A central topic in CS-MRI is how to choose an optimal image transform domain / subspace and the corresponding sparse regularisation. With this purpose in mind, we aim to design a fast yet accurate method to reconstruct high-quality MR images from under-sampled k-space data, this, by combining CS implications and deep learning. |
| Collaborator Contribution | Collaborators provided domain knowledge and expertise and relevant data sets to the project |
| Impact | To early for outputs to be delivered |
| Start Year | 2019 |
| Description | Proximal-Nets: Unfolding Proximal Algorithms for Accelerating and Improving MR Image Reconstruction |
| Organisation | University of Cambridge |
| Department | Department of Applied Mathematics and Theoretical Physics (DAMTP) |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | A central topic in CS-MRI is how to choose an optimal image transform domain / subspace and the corresponding sparse regularisation. With this purpose in mind, we aim to design a fast yet accurate method to reconstruct high-quality MR images from under-sampled k-space data, this, by combining CS implications and deep learning. |
| Collaborator Contribution | Collaborators provided domain knowledge and expertise and relevant data sets to the project |
| Impact | To early for outputs to be delivered |
| Start Year | 2019 |
| Description | Proximal-Nets: Unfolding Proximal Algorithms for Accelerating and Improving MR Image Reconstruction |
| Organisation | University of Cambridge |
| Department | Department of Pure Mathematics and Mathematical Statistics |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | A central topic in CS-MRI is how to choose an optimal image transform domain / subspace and the corresponding sparse regularisation. With this purpose in mind, we aim to design a fast yet accurate method to reconstruct high-quality MR images from under-sampled k-space data, this, by combining CS implications and deep learning. |
| Collaborator Contribution | Collaborators provided domain knowledge and expertise and relevant data sets to the project |
| Impact | To early for outputs to be delivered |
| Start Year | 2019 |
| Description | Research collaboration with University of Edinburgh |
| Organisation | University of Edinburgh |
| Department | Clinical Research Imaging Centre |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | I have provided input into setting up and maintaining the FDG and NaF PET program for imaging atherosclerosis and aortic valve disease. I have co-supervised a PhD student with Professor Newby |
| Collaborator Contribution | Edinburgh has recruited patients for the aortic aneurysm imaging study. The have also shared imaging protocols with us in Cambridge, and we have had exchange sessions at both locations to share and develop ideas for future work and grant applications. |
| Impact | Several grants - see grants section. Several high impact papers Successful co-supervision of PhD student |
| Start Year | 2008 |
| Description | Schoenlieb - Quantitative photoacoustics |
| Organisation | University of Cambridge |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | We have provided expertise in photoacoustic imaging and common reconstruction challenges / artifacts. |
| Collaborator Contribution | Our partner is applying their expertise in image reconstruction to try and overcome common reconstruction challenges / artifacts. |
| Impact | Conference submission has been made based on initial results |
| Start Year | 2016 |
| Description | Sparse regularisation for seismic imaging |
| Organisation | Schlumberger Limited |
| Department | Schlumberger Cambridge Research |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Ongoing collaboration with Dr Evren Yarman on sparse regularisation and optimisation with applications in seismic imaging. We have co-supervised two summer intern students together in the past and will be collaborating with a visiting student from Ecole Polytechnique in 2017. |
| Collaborator Contribution | Know how and funding. |
| Impact | Joint publication in preparation; joint supervision of mathematics students; industrial talk by Evren Yarman to graduate students in mathematics in Cambridge in February 2017. |
| Start Year | 2015 |
| Description | Unveiling the invisible: mathematical methods for cultural heritage |
| Organisation | University of Cambridge |
| Department | The Fitzwilliam Museum |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Hypotheses to be tested: In-depth mathematical analysis of imaging data, developed through our collaboration, could transform the ways in which hypotheses in the study of material culture are tested by searching through algorithmically examined data collected by researchers, revealing hidden patterns in paintings, manuscripts and archaeological objects. Project objectives: We will bring cutting-edge mathematical research to the arts and humanities by focusing on three challenging problems: Textural analysis of cross-sections of paint; Virtual restoration of illuminated manuscripts; Classification of Roman pottery. We will also develop an intuitive software package that will make our methodology accessible to a wide range of arts and humanities scholars. My Cambridge group - in particular my PostDocs Dr Kasia Torgonska and Dr Simone Parisotto are developing the mathematical algorithms for all three cultural heritage applications. They will also be the main developers of the modular toolkit. |
| Collaborator Contribution | Dr Launaro (Faculty of Classics, Cambridge) collaborates on the pottery classification part of the project. With his extensive experience in Roman pottery, their excavation, their historical context, their classification and curation, he is key in defining the shape characteristics in pottery (crucial for their automated classification), in formulating the relevant questions in the different stages of the work, and in critically evaluating the results. He is also responsible for steering the shape analysis and classification capabilities of the modular toolkit and making it user-friendly for a wider use by pottery specialists. Dr Spike Bucklow (Hamilton Kerr Institute (HKI), Cambridge) collaborates on the paint cross section classification, the manuscript restoration and provides input to the modular toolkit, helping define user requirements. He provides the conservation and restoration expertise, in particular on the significance of historic pigments, paints and artists' methods etc. for the manuscript restoration and the art historic significance of technical imagery for the paint cross sections. He has curated the existing database of paint cross-section images at HKI, oversees its development in support the automated cross section classification approaches and supervises its use as a test-bed for the modular toolkit. He is driving all the research questions asked of automated analysis in the manuscript restoration, identifying key visual features of interest in digital images of cross-sections and assessing the relevance and efficiency of software-based recognition of those visual features. As specific discriminatory criteria evolve, he advises on their employment, prioritizing sequences of queries and assessing navigation of the data set. He also advises on how the developed clustering techniques for the paint cross section should be integrated in the modular toolkit. Dr Panayotova (Fitzwilliam Museum) provides the expertise on the illuminated manuscripts through her research on over 4000 illuminated manuscripts at the Fitzwilliam Museum and the Cambridge Colleges. At the start of the project, she identified damaged images and prioritised the most important examples in dated and localised manuscripts for mathematical reconstructions. She selects and provides access to c. 200 from the over 35,000 digital images available from manuscripts in the Fitzwilliam Museum and the Colleges, and c. 100 images acquired with multispectral imaging techniques by the Museum's Research Scientist (at no cost to this project). In the course of the project, she also collaborates on the cross section classification, advising on the historical and artistic background of the selected manuscripts, and providing information on the circumstances of the images' original production and subsequent damage. This represents the human-expert knowledge that will be integrated in the automated restoration. She is giving feedback on the art restoration results, advising on the optimal extent of restoration required to maximise the research potential of the original images. Moreover, Dr Panayotova is co-developing the modular-toolkit, in particular influencing its functionality and design. |
| Impact | Calatroni, Luca; Marie d'Autume and, Rob Hocking ; Panayotova, Stella; Parisotto, Simone; Ricciardi, Paola; Schönlieb, Carola-Bibiane Unveiling the invisible: mathematical methods for restoring and interpreting illuminated manuscripts Journal Article In: Heritage Science, 6 (1), pp. 56, 2018. Parisotto, Simone; Calatroni, Luca; Daffara, Claudia Digital Cultural Heritage imaging via osmosis filtering Inproceedings In: Mansouri A. El Moataz A., Nouboud Mammass F D (Ed.): ICISP 2018: Image and Signal Processing, pp. Springer, 2018. Daffara, Claudia; Parisotto, Simone; Ambrosini, Dario A multipurpose, dual-mode imaging in the MWIR range for artwork diagnostic: a systematic approach Journal Article In: Optics and Lasers in Engineering, 2017. Daffara, Claudia; Parisotto, Simone; Mariotti, Paola Ilaria Mid-infrared thermal imaging for an effective mapping of surface materials and sub-surface detachments in mural paintings: integration of thermography and thermal quasi-reflectography Inproceedings In: Optics for Arts, Architecture, and Archaeology, International Society for Optics and Photonics 2015. Leverhulme Trust project on Unveiling the Invisible - 3years from January 2019; GBP 250K. |
| Start Year | 2013 |
| Description | member of HDAN |
| Organisation | University of Manchester |
| Department | UK Health Data Analytics Network (UK-HDAN) |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | By belonging to this community we are better able to increase awareness of our centre, and benefit from finding out more about ongoing work within the community. Our presence within this enables others to be aware of our work, events etc and possibilities for collaboration. |
| Collaborator Contribution | Thus far we have only just joined, our contribution so far is awareness for others of the work being done within the centre. |
| Impact | Awareness of other work within this field. |
| Start Year | 2017 |
| Title | APPARATUS AND METHOD FOR WIDE-FIELD HYPERSPECTRAL IMAGING |
| Description | Embodiments of the present invention provide a hyperspectral endoscope system, comprising a memory for storing data therein, an endoscope arranged to, in use, receive radiation reflected from a sample and to output wide-field image data and line-scan hyperspectral data corresponding to the sample, a processor coupled to the memory, wherein the processor is arranged, in use, to determine registration information between portions of the wide-field image data, and determine wide-area hyperspectral image data in dependence on the registration information and the line-scan hyperspectral data. |
| IP Reference | US2021374981 |
| Protection | Patent application published |
| Year Protection Granted | 2021 |
| Licensed | Yes |
| Impact | None yet |
| Title | METHODS OF CHARACTERISING AND IMAGING WITH AN OPTICAL SYSTEM |
| Description | A method of characterizing an optical system, wherein the optical system comprises an optical fibre having a proximal end and a distal end, wherein the optical fibre comprises a non-single-mode optical fibre, and comprising a reflector assembly comprising a stack of reflectors disposed at the distal end of the optical fibre, wherein the stack of reflectors is arranged to provide different reflector matrices in dependence on illumination wavelength. The method comprises projecting calibration patterns at a plurality of characterization wavelengths onto the proximal end, then obtaining, at the proximal end, data relating to reflected calibration patterns. An instantaneous transmission matrix of the optical fibre is then determined using the data relating to the reflected calibration patterns and the reflector matrices. |
| IP Reference | US2021386277 |
| Protection | Patent application published |
| Year Protection Granted | 2021 |
| Licensed | Yes |
| Impact | none yet |
| Title | ExVision |
| Description | Ex vivo imaging platform for tissue characterisation |
| Type | Diagnostic Tool - Imaging |
| Current Stage Of Development | Initial development |
| Year Development Stage Completed | 2022 |
| Development Status | Under active development/distribution |
| Impact | none yet |
| Title | HySE |
| Description | Clinical trial provided promising supporting data for further funding applications |
| Type | Diagnostic Tool - Imaging |
| Current Stage Of Development | Refinement. Clinical |
| Year Development Stage Completed | 2020 |
| Development Status | Actively seeking support |
| Clinical Trial? | Yes |
| Impact | Patent filed and publication in preparation |
| Title | Hyperspectral Endoscopy |
| Description | Endoscopy tool for early detection of cancer |
| Type | Diagnostic Tool - Imaging |
| Current Stage Of Development | Refinement. Clinical |
| Year Development Stage Completed | 2020 |
| Development Status | Actively seeking support |
| Clinical Trial? | Yes |
| Impact | First-in-human trial. Developing new approach for future applications. |
| URL | https://clinicaltrials.gov/show/NCT03388047 |
| Title | MuSE |
| Description | Spectral endoscopy |
| Type | Diagnostic Tool - Imaging |
| Current Stage Of Development | Refinement. Clinical |
| Year Development Stage Completed | 2020 |
| Development Status | Under active development/distribution |
| Clinical Trial? | Yes |
| Impact | Application in 20 patient pilot clinical trial led to promising clinical data supporting further funding application and translational activities. One patent in preparation. |
| Title | An Object-Oriented Matlab-Framework for Inverse Problems (OOMFIP) - Version 0.5 |
| Description | Citation Benning, M. An Object-Oriented Matlab-Framework for Inverse Problems (OOMFIP) - Version 0.5 [dataset]. https://doi.org/10.17863/CAM.281 Description This is an early version of a Matlab toolbox for the easier realisation of first-order splitting methods for the solution of non-smooth variational regularisation methods. Software Matlab (R2014a) Subjects Matlab, inverse problems, first-order methods, regularisation |
| Type Of Technology | Software |
| Year Produced | 2016 |
| Open Source License? | Yes |
| Impact | General framework for optimisation in inverse problems. |
| URL | https://www.repository.cam.ac.uk/handle/1810/256338 |
| Title | An R package called SPCAvRP |
| Description | An R package to implement a method for Sparse Principal Component Analysis proposed in Gataric, Wang and Samworth (2017) |
| Type Of Technology | Software |
| Year Produced | 2017 |
| Impact | Too early to say. |
| URL | https://cran.r-project.org/web/packages/SPCAvRP/index.html |
| Title | Anisotropic osmosis filtering for shadow removal in images |
| Description | We present an anisotropic extension of the isotropic osmosis model that has been introduced by Weickert et al. for visual computing applications, and we adapt it specifically to shadow removal applications. We show that in the integrable setting, linear anisotropic osmosis minimises an energy that involves a suitable quadratic form which models local directional structures. In our shadow removal applications we estimate the local structure via a modified tensor voting approach and use this information within an anisotropic diffusion inpainting that resembles edge-enhancing anisotropic diffusion inpainting. Our numerical scheme combines the nonnegativity preserving stencil of Fehrenbach and Mirebeau with an exact time stepping based on highly accurate polynomial approximations of the matrix exponential. The resulting anisotropic model is tested on several synthetic and natural images corrupted by constant shadows. We show that it outperforms isotropic osmosis, since it does not suffer from blurring artefacts at the shadow boundaries. |
| Type Of Technology | Software |
| Year Produced | 2018 |
| Open Source License? | Yes |
| Impact | Significantly improved shadow removal in digital images; reduces artefacts. |
| URL | https://iopscience.iop.org/article/10.1088/1361-6420/ab08d2 |
| Title | Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation |
| Description | This code allows to reproduce the results of Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation |
| Type Of Technology | Software |
| Year Produced | 2017 |
| Open Source License? | Yes |
| Impact | Superresolution of spectral imaging data by using structure from high-res aerial photograph; the same principle can be applied to super resolution in medical imaging (follow on projects related to this are underway). |
| URL | http://iopscience.iop.org/0266-5611/34/4/044003/ |
| Title | Gradient descent in a generalised Bregman distance framework |
| Description | Research data supporting the paper https://arxiv.org/abs/1612.02506 Martin Benning, Marta M. Betcke, Matthias J. Ehrhardt, Carola-Bibiane Schönlieb "Gradient descent in a generalised Bregman distance framework" [dataset]. https://doi.org/10.17863/CAM.6489 This data contains the corresponding MATLAB©-code for the numerical examples in the conference proceedings paper 'Gradient descent in a generalised Bregman distance framework'. Download the zip-file and extract it to a folder of your choice. Execute the 'setpath.m' file to add all relevant files to the MATLAB© path, and switch to the folder 'Examples'. This folder contains a script named 'phasereconstruction.m' that will compute the numerical examples as presented in the paper. A detailed explanation of the script can be found in terms of the HTML-file 'phasereconstruction.html' in the sub-folder 'Manual'. |
| Type Of Technology | Software |
| Year Produced | 2016 |
| Open Source License? | Yes |
| Impact | Bregman iteration framework for non-convex and non-smooth optimisation. |
| URL | https://www.repository.cam.ac.uk/handle/1810/261315 |
| Title | Image enhancement using GAN network with real-order derivative induced loss functions |
| Description | A generative adversarial network (GAN) for image enhancement equipped with loss functions (RDL) induced by real-order derivative operators. The new network features a discriminator network that is trained to differentiate between the enhanced images and ground-truth images, and a new loss function motivated by real-order derivative operators, which is capable of capturing global differences. |
| Type Of Technology | Software |
| Year Produced | 2019 |
| Open Source License? | Yes |
| Impact | To our knowledge, it is the first framework that incorporates non-integer order derivative operators in loss functions. |
| URL | https://arxiv.org/pdf/1805.06761.pdf |
| Title | Mathematical Imaging Methods for Mitosis Analysis in Live-Cell Phase Contrast Microscopy |
| Description | We propose a workflow to detect and track mitotic cells in time-lapse microscopy image sequences. In order to avoid the requirement for cell lines expressing fluorescent markers and the associated phototoxicity, phase contrast microscopy is often preferred over fluorescence microscopy in live-cell imaging. However, common specific image characteristics complicate image processing and impede use of standard methods. Nevertheless, automated analysis is desirable due to manual analysis being subjective, biased and extremely time-consuming for large data sets. Here, we present the following workflow based on mathematical imaging methods. In the first step, mitosis detection is performed by means of the circular Hough transform. The obtained circular contour subsequently serves as an initialisation for the tracking algorithm based on variational methods. It is sub-divided into two parts: in order to determine the beginning of the whole mitosis cycle, a backwards tracking procedure is performed. After that, the cell is tracked forwards in time until the end of mitosis. As a result, the average of mitosis duration and ratios of different cell fates (cell death, no division, division into two or more daughter cells) can be measured and statistics on cell morphologies can be obtained. All of the tools are featured in the user-friendly MATLAB® Graphical User Interface MitosisAnalyser. Reference: Joana Sarah Grah, Jennifer Alison Harrington, Siang Boon Koh, Jeremy Andrew Pike, Alexander Schreiner, Martin Burger, Carola-Bibiane Schönlieb, Stefanie Reichelt. "Mathematical imaging methods for mitosis analysis in live-cell phase contrast microscopy." Methods 115 (2017): 91-99. |
| Type Of Technology | Software |
| Year Produced | 2017 |
| Open Source License? | Yes |
| Impact | MitosisAnalyser is a software tool with an easy to use graphical user interface for cancer researchers without prior knowledge on image analysis to use. |
| URL | https://github.com/JoanaGrah/MitosisAnalyser |
| Title | Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation |
| Description | Paper: [SIAM.org][arxiv] Authors: M. Betcke and M. Ehrhardt Abstract: Magnetic resonance imaging (MRI) is a versatile imaging technique that allows different contrasts depending on the acquisition parameters. Many clinical imaging studies acquire MRI data for more than one of these contrasts---such as for instance T1 and T2 weighted images---which makes the overall scanning procedure very time consuming. As all of these images show the same underlying anatomy one can try to omit unnecessary measurements by taking the similarity into account during reconstruction. We will discuss two modifications of total variation---based on i) location and ii) direction---that take structural a priori knowledge into account and reduce to total variation in the degenerate case when no structural knowledge is available. We solve the resulting convex minimization problem with the alternating direction method of multipliers that separates the forward operator from the prior. For both priors the corresponding proximal operator can be implemented as an extension of the fast gradient projection method on the dual problem for total variation. We tested the priors on six data sets that are based on phantoms and real MRI images. In all test cases exploiting the structural information from the other contrast yields better results than separate reconstruction with total variation in terms of standard metrics like peak signal-to-noise ratio and structural similarity index. Furthermore, we found that exploiting the two dimensional directional information results in images with well defined edges, superior to those reconstructed solely using a priori information about the edge location. |
| Type Of Technology | Software |
| Year Produced | 2016 |
| Open Source License? | Yes |
| Impact | Software for joint-reconstruction for multi contrast MRI |
| URL | http://www.damtp.cam.ac.uk/research/cia/software/ |
| Title | Preconditioned ADMM with nonlinear operator constraint |
| Description | Abstract: We are presenting a modification of the well-known Alternating Direction Method of Multipliers (ADMM) algorithm with additional preconditioning that aims at solving convex optimisation problems with nonlinear operator constraints. Connections to the recently developed Nonlinear Primal-Dual Hybrid Gradient Method (NL-PDHGM) are presented, and the algorithm is demonstrated to handle the nonlinear inverse problem of parallel Magnetic Resonance Imaging (MRI). Reference: Martin Benning, Florian Knoll, Carola-Bibiana Schönlieb und Tuomo Valkonen, Preconditioned ADMM with nonlinear operator constraint, IFIP conference proceedings 2015. |
| Type Of Technology | Software |
| Year Produced | 2015 |
| Open Source License? | Yes |
| Impact | Robust method for optimisation for nonlinear inverse problems. |
| URL | https://www.repository.cam.ac.uk/handle/1810/256221 |
| Title | Stochastic PDHG with Arbitrary Sampling and Imaging Applications |
| Description | This package contains an ODL compatible implementation of the Stochastic Primal-Dual Hybrid Gradient algorithm (SPDHG) proposed and analyzed in our associated publication. It is useful to solve inverse problems with non-smooth regularisation and large-scale data. SPDHG is a direct generalization of the popular Primal-Dual Hybrid Gradient algorithm (PDHG) also known as the Chambolle-Pock algorithm. |
| Type Of Technology | Software |
| Year Produced | 2018 |
| Open Source License? | Yes |
| Impact | New EPSRC project on Clinical PET reconstruction. |
| URL | https://epubs.siam.org/doi/10.1137/17M1134834 |
| Description | Conference: Developments in Healthcare Imaging - Connecting with Industry |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Industry/Business |
| Results and Impact | A user engagement event was held, showcasing the work of the CMIH, with numerous industrial contacts present. The event gave a great opportunity for clinicians, academics and industrial partners to network. Many participants provided feedback on how useful this opportunity was, and how they had hugely beneficial conversations within the networking opportunities (lunch and coffee breaks, and a poster reception), as well as learning more about the centres work through the talks. Some potential industrial collaborators attended, and are looking to further their connections with the centre. |
| Year(s) Of Engagement Activity | 2016 |
| URL | https://www.turing-gateway.cam.ac.uk/event/tgmw37 |
| Description | 21st ECMI Conference on Industrial and Applied Mathematics (ECMI 2021) |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | The series of European Consortium for Mathematics in Industry (ECMI) conferences are devoted to enforcing the interaction between academy and industry, leading to innovations in both fields. These events have attracted leading experts from business, science, and academia, and have promoted the application of novel mathematical technologies to industry. We hope that ECMI 2021 will further enhance multidisciplinary research and development both in academia and industry, leading to the formulation of challenging real-life problems, where mathematics may provide significant new insights and at the same time may be inspired by those interactions. |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://ecmiindmath.org/2020/11/22/21st-ecmi-conference-on-industrial-and-applied-mathematics/ |
| Description | 26th UK Conference on Medical Image Understanding and Analysis |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Postgraduate students |
| Results and Impact | MIUA is a UK-based international conference for the communication of image processing and analysis research and its application to medical imaging and biomedicine. This is a rapidly growing subject with ever increasing real-world applicability. MIUA welcomes all researchers in medical imaging including mathematicians, computer scientists, bioinformaticians, clinicians, engineers and bioscientists. MIUA is the principal UK forum for communicating research progress within the community interested in image analysis applied to medicine and related biological science. The meeting is designed for the dissemination and discussion of research in medical image understanding and analysis, and aims to encourage the growth and raise the profile of this multi-disciplinary field by bringing together the various communities. MIUA covers many topics in medical imaging including: |
| Year(s) Of Engagement Activity | 2022 |
| URL | https://www.miua2022.com/ |
| Description | 91st GAMM Annual Meeting |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | The Annual Meeting of the International Association of Applied Mathematics and Mechanics, the GAMM 2020@21. |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://jahrestagung.gamm-ev.de/jahr2020-2021/annual-meeting/ |
| Description | Achieving Impact in Healthcare: From Mathematics to Clinical Support Systems and Devices |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Postgraduate students |
| Results and Impact | This joint workshop of the five Centres focused on translating mathematical research into technological advances, as well as outreach and linkage with clinicians and end-user companies. It presented the opportunity to hear in detail about the project collaborations, research and outcomes from each Centre. The programme aimed not only to nurture the mathematical research associated with the Centres, but to engage end-users to ensure that best practice is spread as widely as possible. The Programme featured talks from all five Centres. The themes of 'Clinical Support Systems', 'Population Medicine ' and 'Mathematical Challenges" were explored. Talks covered a range of topics, including cross-methodology challenges for specific disease groups, cross-disease challenges for specific methodologies and machine learning customised for medical imaging. This workshop also aimed to coordinate and consolidate the research agenda within the Maths for Healthcare space for the subsequent five years and scope out a proposal for a six month Research Programme on the Mathematics of Healthcare to be held at the Isaac Newton Institute. The event was of interest to researchers, clinicians and healthcare technologists from biomedical imaging, mathematics, engineering, computer science, biology and medicine and presents the opportunity for knowledge exchange and networking between senior scientists with relevant individuals from industry and government. |
| Year(s) Of Engagement Activity | 2019 |
| URL | https://gateway.newton.ac.uk/event/tgmw63 |
| Description | Artificial Intelligence Developments in Healthcare Imaging |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | This user engagement event focussed on artificial intelligence and provided an update on some of the research projects and collaborations taking place in the CMIH. It featured presentations from CMIH researchers and Industry Partners, as well as other academics and end-users in the public sector and industry. A number of industry challenges and collaborations were highlighted in an elevator pitch session. The event was of interest to researchers working in the field of acquisition and analysis of clinical imaging and also to healthcare planners, clinicians, policy makers, and industry partners to discuss the research projects and challenges arising from the area. It presented the opportunity for knowledge exchange and networking between senior scientists from areas such as mathematics, statistics, engineering, physics and biomedicine and relevant individuals from industry and government. |
| Year(s) Of Engagement Activity | 2019 |
| URL | https://gateway.newton.ac.uk/event/tgmw70 |
| Description | Artificial Intelligence and Machine Learning in Clinical Imaging Research: Progress and Promise |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | The National Institute for Health Research (NIHR) and the EPSRC Centre for Mathematical Imaging in Healthcare (CMIH) held this event to consider how to accelerate translation of artificial intelligence into clinical practice. |
| Year(s) Of Engagement Activity | 2018 |
| URL | https://gateway.newton.ac.uk/event/tgmw64 |
| Description | BIRS Women in Inverse Problems |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | The objective of this workshop is to bring together women in the broad and vibrant field of Inverse Problems. Both established as well as early career researchers will come together to discuss their recent research achievements. This workshop will facilitate professional networking and create mentoring opportunities for women researchers. The ultimate goal is to help broaden female participation in research careers in particular in the field of Inverse Problems, as well as to create new research collaborations. |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://www.birs.ca/events/2021/5-day-workshops/21w5035 |
| Description | BIRS Workshop on Optimal Transport meets Probability, Statistics and Machine Learning, 30 April - 5 May 5, 2017, Oaxaca, Mexico. |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Organizers Guillaume Carlier (Université Paris Dauphine) marco cuturi (ENSAE) Brendan Pass (University of Alberta) Carola-Bibiane Schönlieb (University of Cambridge) Objectives 1) Optimal transport has long standing connections to probability, which have been amplified in recent years. For example, variants of the classical optimal transport problem have arisen in connection to applications in financial mathematics (including transport problems with additional dependence constraints and martingale optimal transport, where versions with several, or even infinitely many, prescribed marginals are also of interest) and Schrödinger's problem of minimizing the relative entropy of stochastic processes with fixed initial and final laws. In addition, Wasserstein barycenters have recently been developed as a natural tool to average or interpolate among several probability measures, against the background geometry of optimal transport. This extends the celebrated notion of displacement interpolation between two measures, and has recently found many fruitful applications, in image processing, economics and statistics. Each of these problems brings forth significant new challenges, both theoretical and computational; in addition to addressing these, we have strong reasons to believe that bringing together leaders in optimal transport with experts in probability might uncover even more connections and will stimulate research on both sides. 2) The interest in multi-marginal optimal transport problems is also rapidly growing, driven in particular by its connection with density functional theory in quantum chemistry and fluid dynamics (Brenier's generalized solutions of incompressibe Euler). Understanding the structure, regularity and sparsity properties of optimal plans for multi-marginal transport problems is a very active and challenging area of research. Fast numerical solvers yet are still to be found to address these typically very high-dimensional problems. One of our goals in gathering specialists of optimal transport (both theoretical and computational), probability and statistics in a broad sense is to better understand how, for instance, Markov Chain Monte Carlo methods could help overcome such a computational bottleneck. 3) As outlined above, OT methods are rapidly developing in statistics and econometrics, one reason-among many others being for instance that the Brenier's map may be viewed as one of the most natural multivariate extensions of the notion of quantile. Statistical inference based on Wasserstein distances is therefore becoming more and more popular. However, there are few rigorous limit theorems (apart from the real line case) which fully justify its use and one aim of this workshop is to make progress on such delicate issues which intimately connect probability, analysis and the geometry of Wasserstein spaces. 4)Representation of datasets, classification and measurement of similarities/disparities between complex data or objects such as images or collection of histograms are ubiquitous problems in machine learning. Optimal transport based distances are used more and more frequently to address these questions. For instance in principal component analysis (PCA) one aims to approximate in the most accurate way a large cloud of points by a small dimensional manifold and geodesic Wasserstein PCA is becoming a popular tool for large collections of histograms. Another important problem is metric learning which can somehow be viewed as a sort of inverse transport problem: what can be infered on the distance between objects given an observed coupling between them? Bringing specialists of questions using optimal transport methods, it is our hope to have a clearer picture and better geometric and analytic understanding on the performances and complexity of computational OT based methods in machine learning. |
| Year(s) Of Engagement Activity | 2017 |
| URL | http://www.birs.ca/events/2017/5-day-workshops/17w5093 |
| Description | Big Data in Medicine Conference |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Third sector organisations |
| Results and Impact | A Big data in Medicine conference was co-hosted by the CMIH, attended by a variety of academics, industrial partners, students, and clinicians. |
| Year(s) Of Engagement Activity | 2017 |
| URL | https://www.bigdata.cam.ac.uk/events/events-archive/2017-events/copy_of_big-data-in-medicine-confere... |
| Description | Big Data, Multimodality & Dynamic Models in Biomedical Imaging |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Other audiences |
| Results and Impact | The CMIH were heavily involved in a conference bringing together big data and medicine, both centre directors were involved in the planning and organisation. The conference bought together potential partners and industry specialist, leading to future engagement. |
| Year(s) Of Engagement Activity | 2016 |
| URL | https://www.turing-gateway.cam.ac.uk/event/tgmw32 |
| Description | British Science Association Media Fellowship |
| Form Of Engagement Activity | A magazine, newsletter or online publication |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | In 2017, I was awarded a prestigious British Science Association Media Fellowship. I spent 4 weeks working as a full-time science correspondent at The Guardian newspaper. This experience sharpened my writing and given me valuable insights into how a leading media organisation reports science to the public. |
| Year(s) Of Engagement Activity | 2017 |
| URL | https://www.theguardian.com/profile/james-rudd |
| Description | Building Machine learning models for expensive Forward Problems in Medical Imaging. |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | I presented a poster with the title "Surrogate Model for The Forward Problem Associated with EEG". |
| Year(s) Of Engagement Activity | 2018 |
| Description | CAMBRIDGE SCIENCE FESTIVAL: Harnessing big clinical data in medicine: can AI improve Breast Cancer Screening? |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Public/other audiences |
| Results and Impact | 2.2 million women are screened for breast cancer each year in the UK. Can AI identify women at most risk of cancer, improve the performance of the radiologists reading the mammograms or even replace the readers? |
| Year(s) Of Engagement Activity | 2020 |
| URL | https://www.sciencefestival.cam.ac.uk/events/limited-tickets-available-door-harnessing-big-clinical-... |
| Description | CANADIAN APPLIED AND INDUSTRIAL MATHEMATICS SOCIETY 2021 Annual Meeting |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Each year CAIMS/SCMAI hosts an annual meeting for all members. This meeting is one of the central activities of CAIMS and has been held for over 30 years. The annual meeting covers all areas of applied and industrial mathematics with high profile speakers invited to give keynote addresses on currently active thematic areas. |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://uwaterloo.ca/canadian-applied-industrial-math-society-annual-meeting-2021/ |
| Description | CCIMI New Directions in the Mathematics of Information, Thursday 10th November 2016 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Thursday 10th November 2016 Background Launched in May this year, the Cantab Capital Institute for the Mathematics of Information (CCIMI) is now fully operational with research studentships and a number of exciting collaborative projects underway. Established through philanthropic support of £5 million from Cantab Capital Partners, the Institute accommodates research activity on fundamental mathematical problems and methodology for understanding, analysing, processing and simulating data. The data science research performed in the Institute is at the highest international level, with a key aim to extract relevant information from large and high-dimensional data with a predictable certainty. The advance of data science and the solution of big data questions heavily relies on fundamental mathematical techniques and in particular, their intra-disciplinary engagement. This is at the heart of the Institute, involving mathematical expertise ranging from statistics, applied and computational analysis, to topology and discrete geometry - all with the common goal of advancing data science questions. This event provided an opportunity for a more detailed update on current research taking place at the Institute, associated challenges and other potential collaborative opportunities. Aims and Objectives Current research projects being undertaken by the Institute encompass a variety of activity, from the development of mathematical and statistical tools and techniques for high-dimensional data analysis to hybrid image reconstruction and analysis models. As well as the development of rigorous machine learning methodologies that are accessible by mathematical and statistical analysis techniques. Applications of interest include adaptive image analysis, such as image classification, segmentation and enhancement, all the way to inverse problems in industrial and medical imaging. This event provided an update to the original launch in May, providing more detailed information on research projects and collaborations, as well as highlighting potential new ones. It featured presentations of some of the current project collaborations. The Institute currently has 11 projects: Formation and Adaptation of Biological Transportation Networks Statistical and Computational Aspects of Aggregating Data Summaries Mathematics of Machine Learning: Mathematical Learning Methods for Adaptive and Robust Data Analysis Mixing Times Denoising Geodesic Ray Transforms Mathematical challenges in Electron Tomography Mathematical Challenges of Large Environmental Data Sets Computational and Statistical Joint Image Analysis Statistical Applications of Persistent Homology Bayesian Inference for Discretely Sampled Diffusions - Solving the Nonlinear Inverse Problem To Create a Semantic Search Engine for Mathematical Literature For more information on these projects please visit the CCIMI Website. The Institute's research and projects cover a diverse range of areas across Big Data and so we expect this event to be of interest to individuals from multiple researcher, industry and public communities. Presentations were followed by a drinks and networking reception and the afternoon finished with a Public Lecture by David Spiegelhalter. A copy of the programme can be found here. |
| Year(s) Of Engagement Activity | 2016 |
| URL | http://www.turing-gateway.cam.ac.uk/event/tgmw38 |
| Description | CMIH Imaging Clinic |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Professional Practitioners |
| Results and Impact | The CMIH hosts an Imaging Clinic, where members of the University, clinicians, industrial partners can drop in for advice on imaging related problems and to discuss potential collaborations. The Imaging Clinic initiative was extended to the Imaging and Mathematics network in 2020. |
| Year(s) Of Engagement Activity | 2017,2018,2019 |
| URL | https://www.cmih.maths.cam.ac.uk/imaging-clinic/ |
| Description | CMIH Imaging Clinic |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Postgraduate students |
| Results and Impact | The CMIH hosts an Imaging Clinic, here members of the University, Clinicians and Industrial partners can drop in for advice on imaging related problems and to discuss potential collaborations |
| Year(s) Of Engagement Activity | 2018,2019 |
| URL | https://www.cmih.maths.cam.ac.uk/imaging-clinic/ |
| Description | CMIH Imaging Clinic |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Postgraduate students |
| Results and Impact | The CMIH hosts an imaging clinic, where member of the University and Industrial partners can drop in for advice on imaging related problems, and discuss potential collaborations. |
| Year(s) Of Engagement Activity | 2017 |
| URL | https://www.cmih.maths.cam.ac.uk/imaging-clinic/ |
| Description | CMIH Imaging Clinic / Imaging Newtork |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Postgraduate students |
| Results and Impact | The CMIH hosts an Imaging Clinic, here members of the University, Clinicians and Industrial partners can drop in for advice on imaging related problems and to discuss potential collaborations. This has been extended to create the Imaging and Mathematics network; this encourages researchers from across the Cambridge network to get together and discuss their work or present problems or novel solutions to problems to others and share discussions and knowledge. |
| Year(s) Of Engagement Activity | 2018,2019,2020 |
| URL | https://www.cmih.maths.cam.ac.uk/imaging-network/ |
| Description | CMIH Quarterly newsletter |
| Form Of Engagement Activity | A magazine, newsletter or online publication |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Supporters |
| Results and Impact | A quarterly newsletter is sent to all who have signed up via our website (currently just over 150). This gives a large spectrum of engagement, including those in industry, the NHS, academia and beyond. The newsletter led to further followers of or twitter feed, and diagnostics show links to larger articles on our activities, and to future events were accessed via the newsletter. |
| Year(s) Of Engagement Activity | 2018,2019,2020 |
| Description | CMIH Quarterly newsletter |
| Form Of Engagement Activity | A magazine, newsletter or online publication |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Supporters |
| Results and Impact | A quarterly newsletter is sent to all who have signed up via our website (currently just over 130), this gives a large spectrum of engagement, including those in industry, the nhs, academia and beyond. The newsletter led to further followers of or twitter feed, and diagnostics show links to larger articles on our activities, and to future events were accessed via the newsletter. |
| Year(s) Of Engagement Activity | 2018,2019,2020 |
| URL | https://mailchi.mp/2afeebd9dbc1/cmih-autumn-2017-newsletter-1580093 |
| Description | CMIH Twitter Feed |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Other audiences |
| Results and Impact | A twitter account has been maintained, which reports on the work of the centre, advertises future events, showcases news items, and creates a social media presence for the centre. Posts seem to provide further sign-ups to the website/newsletter (as well as vice versa) and produce more followers and retweets. Currently (March 2021) there are 428 followers, from a variety of countries |
| Year(s) Of Engagement Activity | 2018,2019,2020,2021,2022 |
| URL | https://twitter.com/CambridgeCMIH |
| Description | CMIH Twitter feed |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Other audiences |
| Results and Impact | A twitter account has been established and maintained, which reports on the work of the centre, advertises future events, showcases news items, and creates a social media presence for the centre. Posts seem to provide further sign ups to the website/newsletter (as well as vice versa), and produce more followers and retweets. Currently (March 2019) there are 237 followers, from a variety of countries |
| Year(s) Of Engagement Activity | 2018,2019 |
| URL | https://twitter.com/CambridgeCMIH |
| Description | CMIH twitter feed |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Other audiences |
| Results and Impact | A twitter account has been established and maintained, which reports on the work of the centre, advertises future events, showcases news items, and creates a social media presence for the centre. Posts seem to provide further sign-ups to the website/newsletter (as well as vice versa) and produce more followers and retweets. Currently (March 2019) there are 300 followers, from a variety of countries |
| Year(s) Of Engagement Activity | 2018,2019,2020 |
| URL | https://twitter.com/CambridgeCMIH |
| Description | CMIH website |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | The CMIH website is an online permanent presence of the centre and is continually updated. It showcases research publications, current and future projects, news items on activities from within - and beyond - the centre and provides information on the centre to interested parties/individuals. To date, there have been over 15700 visitors to the website. |
| Year(s) Of Engagement Activity | 2018,2019,2020 |
| URL | http://cmih.maths.cam.ac.uk/ |
| Description | CMIH website |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | The CMIH website an online permanent presence of the centre and is continually updated. It showcases current and future projects, news items on activities from within - and beyond - the centre and provides information on the centre to interested parties/individuals. During 2018 there were over 5800 visitors to the website over the last 12 months. |
| Year(s) Of Engagement Activity | 2018,2019,2020 |
| URL | http://cmih.maths.cam.ac.uk/ |
| Description | CMIH website |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | The CMIH website is an online presence of the centre and was continually updated until March 2021. The site is now archived and accessible via a new group website which will be going live very soon. It showcases research publications, current and future projects, news items on activities from within - and beyond - the centre and provides information on the centre to interested parties/individuals. To date, there have been over 17300 visitors to the website. |
| Year(s) Of Engagement Activity | 2018,2019,2020,2021,2022 |
| URL | https://archive.cmih.maths.cam.ac.uk/ |
| Description | CMIH website |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | Launch and continual updating of Centre Website, to showcase current and future projects, add news items on acitvities within (and beyond) centre, and provide information on centre to those interested. During 2017 there were over 4500 visits to the website, 58% of which were new visitors. |
| Year(s) Of Engagement Activity | 2016,2017 |
| URL | http://cmih.maths.cam.ac.uk/ |
| Description | CRUK-AACR Physical Sciences in Oncology |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Talk followed by interactive panel session |
| Year(s) Of Engagement Activity | 2019 |
| Description | Cambridge Research Magazine |
| Form Of Engagement Activity | A magazine, newsletter or online publication |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | The Cambridge Research Magazine - Research Horizons - ran an article on our research. This focused on the use of statistics and linguistics to recreate ancient languages. This was picked up by international media and the Daily Mail ran a long article on the ideas (see other entry). |
| Year(s) Of Engagement Activity | 2016 |
| URL | https://issuu.com/uni_cambridge/docs/issue_30_research_horizons/1?e=1892280/36314892 |
| Description | Cambridge Science Festival |
| Form Of Engagement Activity | Participation in an open day or visit at my research institution |
| Part Of Official Scheme? | Yes |
| Type Of Presentation | Poster Presentation |
| Geographic Reach | National |
| Primary Audience | Schools |
| Results and Impact | We demonstrated several aspects of our research, through video, posters and direct discussion. High level of interest in participating in medical research and science generally. |
| Year(s) Of Engagement Activity | 2012 |
| Description | Cambridge Science Festival |
| Form Of Engagement Activity | Participation in an open day or visit at my research institution |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Public/other audiences |
| Results and Impact | The Cambridge Science Festival is a very well attended event each year. We presented a linguistic demonstration based on speaking ancient languages and showed how this related to mathematics. We also mentioned how this could be related to other mathematical problems such as those arising in Imaging. |
| Year(s) Of Engagement Activity | 2016 |
| Description | Cantab Capital Institute for the Mathematics of Information -- Launch Event, 9 May 2016, Isaac Newton Institute, Cambridge, UK. |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | This event celebrated the launch of an exciting new research Institute which is a collaboration between the Cantab Capital Partners LLP and the University of Cambridge. Hosted within the Faculty of Mathematics of the University of Cambridge, the Cantab Capital Institute for the Mathematics of Information will push the boundaries of information science. Established through philanthropic support of £5 million from Cantab Capital Partners, the Institute will accommodate research activity on fundamental mathematical problems and methodology for understanding, analysing, processing and simulating data. Data science research performed in the Institute will be on the highest international level, aiming to extract the relevant information from large-and high-dimensional data with a predictable certainty. At the heart of the Institute, will be the fundamental mathematical techniques and their intra-disciplinary engagement, upon which, the solution of big data questions so heavily relies. This is crucial in order to ensure advancements in data science. Aims and Objectives This launch event provided an opportunity to learn more about the work of the Institute, such as the specific questions that feed into fundamental methodology development. It is anticipated that the research will focus on various applications across a number of interdisciplinary engagements. These could include for instance, economists and social scientists on questions about financial markets and the internet, or with physicists and engineers on software and hardware development questions in the context of security. Presentations at the event introduced areas of mathematical expertise represented in the Institute and outline how fundamental techniques can be drawn on to meet the challenge of deciphering meaning in the ever growing volumes of data. Academic expertise at the Institute includes: Statistics Applied and Computational Analysis Stochastic Analysis and Probability Inverse Problems Convex Analysis Stochastic and Sparse Optimisation Compressed Sensing and Sampling Theory Partial Differential Equations Number Theory Quantum Computing, Cryptography and Communication The highlight of the launch was the inaugural lecture of the Institute given by Professor Ronald DeVore from Texas A&M University. Ron is one of the key figures of modern applied mathematics and made substantial contributions to approximation theory, numerical analysis of partial differential equations, wavelet transforms, machine learning algorithms and the theory of compressive sensing. |
| Year(s) Of Engagement Activity | 2016 |
| URL | http://www.turing-gateway.cam.ac.uk/event/tgmw34 |
| Description | Celebrating Chris Budd: A leader in mathematical innovation turns 60 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | On the 4th of March 2020 we came together at the University of Bath and celebrate the 60th birthday of our colleague, friend and collaborator Chris Budd. The aim of this workshop was to bring together collaborators, former PhD students and PostDocs of Chris to celebrate him and his achievements in mathematical innovation over his long and fruitful academic career. |
| Year(s) Of Engagement Activity | 2019 |
| URL | https://sites.google.com/view/chrisbudd60/home |
| Description | Classic and Deep Vision for Healthcare Image Analysis |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | "In this tutorial, we will start by drawing attention to the current state of developments for healthcare image analysis. We will start by introducing the topic and giving an overview of recent developments in the area. We will then present current challenges when dealing with medical data focusing on three major topics: learning with unlabelled data, interpretable machine learning and transfer learning. We will close our tutorial by summarising the current challenges and opportunities in this domain. Some open questions related to the topic will also be discussed in the end." |
| Year(s) Of Engagement Activity | 2019 |
| URL | https://sites.google.com/view/acpr19healthcare |
| Description | Computational Mathematics and Machine Learning |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | The aim of this workshop is to formulate a plan for future developments within the area of computational science and engineering (CSE) making use of machine learning techniques. We will discuss the impact that machine learning has already made or will make on computational mathematics, and how the ideas from computational mathematics, particularly numerical analysis, can be used to help understanding and better formulating machine learning models. In the annex, the state of the art is provided in more detail. The question is: which research directions are most promising? What should we concentrate on? How can we combine physics-based and data-based techniques? Can we formulate joint projects? Or maybe a joint organisation for the discussion and dissemination of new developments? In this workshop, we will address the following two very important questions: (1) How machine learning has already impacted and will further impact computational mathematics, scienti?c computing and computational science? (2) How computational mathematics, particularly numerical analysis, can impact machine learning? To accomplish the aforementioned aim, in this workshop, we review what has been learned on these two issues. We will discuss some of the most important progress that has been made on the foregoing two issues, and where new developments should take place. This workshop will be considered a success if we have been able to put things into a perspective that will help to integrate machine learning with computational mathematics, and produced (at the end of the workshop) a sound plan for future research directions in several of the areas mentioned in Section 4. We will identify the most promising research directions, networking activities, as well as building of new collaborations between participants. |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://www.lorentzcenter.nl/computational-mathematics-and-machine-learning.html |
| Description | Convergent Science in Surgical Oncology |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Conference presentation |
| Year(s) Of Engagement Activity | 2019 |
| Description | Daily Mail toothpaste interview |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | Long interview about NaF PET imaging with the Daily Mail. |
| Year(s) Of Engagement Activity | 2015 |
| URL | http://www.dailymail.co.uk/health/article-3158692/Cheap-easy-toothpaste-test-spots-risk-stroke-heart... |
| Description | Deep Learning and Inverse Problems (NeurIPS Workshop) |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | This virtual workshop aims at bringing together theoreticians and practitioners in order to chart out recent advances and discuss new directions in deep learning-based approaches for solving inverse problems in the imaging sciences and beyond. |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://deep-inverse.org/index.html |
| Description | Departmental Seminar, University of Toronto Mississauga |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Professional Practitioners |
| Results and Impact | Virtual talk |
| Year(s) Of Engagement Activity | 2020 |
| Description | Developments in Healthcare Imaging - Connecting with Academia 2017 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Postgraduate students |
| Results and Impact | An annual workshop was hosted to share the latest academic insights and work in the field. |
| Year(s) Of Engagement Activity | 2017 |
| URL | https://www.turing-gateway.cam.ac.uk/event/tgmw42 |
| Description | Developments in Healthcare Imaging - Connecting with Academia 2018 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Postgraduate students |
| Results and Impact | An annual event that brought together academics working on advances in imaging technology with researchers who investigate new image analysis methods, to help address current challenges. This event presented the opportunity to hear in detail about some of the current project collaborations, and focused on the academic interactions taking place in the field of medical imaging and especially across the EPSRC Centres for Mathematical Sciences in Healthcare. |
| Year(s) Of Engagement Activity | 2018 |
| URL | https://gateway.newton.ac.uk/event/tgmw58 |
| Description | Developments in Healthcare Imaging - Connecting with Industry 2017 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Industry/Business |
| Results and Impact | The CMIH hosted an annual engagement workshop, attended by industrial partners, clinicians, and academics working in the field |
| Year(s) Of Engagement Activity | 2017 |
| URL | https://www.turing-gateway.cam.ac.uk/event/tgmw48 |
| Description | Developments in Healthcare Imaging - Connecting with Industry 2018 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Industry/Business |
| Results and Impact | The CMIH hosted an annual engagement workshop, attended by industrial partners, clinicians, and academics working in the field. This user engagement day provided an update on some of the research projects and collaborations taking place in the CMIH. It featured presentations from CMIH researchers and Industry Partners and a number of industry challenges and potential new collaborations were highlighted in an elevator pitch session. |
| Year(s) Of Engagement Activity | 2018 |
| URL | https://gateway.newton.ac.uk/event/tgmw61 |
| Description | Digital Media Editor, Heart (BMJ) |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Digital Media Editor at Heart, running podcasts and social media to engage practitioners. |
| Year(s) Of Engagement Activity | 2015,2016,2017,2018 |
| URL | http://heart.bmj.com/ |
| Description | Duke University Visit |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Study participants or study members |
| Results and Impact | Bogdan Roman was invited for an extended visit (12 days) at Duke University to broaden the reach of the joint work on image acquisition |
| Year(s) Of Engagement Activity | 2019 |
| Description | EPSRC Five Centres Maths In Healthcare Meeting |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Postgraduate students |
| Results and Impact | The event was jointly hosted by the Liverpool and Glasgow Centres and brought together each of the five EPSRC funded groups - Cambridge, Exeter, Glasgow, Liverpool and London. Participants including researchers, clinicians and industry stakeholders engaged and discussed their research, continuing the theme of collaboration in addressing current healthcare challenges. Representatives from the EPSRC attended and provided their perspective on the mid-term reviews and discussed the way forward. |
| Year(s) Of Engagement Activity | 2018 |
| URL | https://www.cmih.maths.cam.ac.uk/epsrc-five-centres-maths-healthcare-meeting/ |
| Description | Eighth International Conference on Scale Space and Variational Methods in Computer Vision (SSVM) |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | SSVM is a biannual meeting within the area of Computer Vision and Image Analysis. SSVM focuses especially on multiscale analysis of image content, partial differential equations, geometric and level-set methods, variational methods, and optimization. |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://ssvm2021.sciencesconf.org/ |
| Description | Emerging Analytical Professionals |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | Talk to approximately 100 individuals from a diverse background, including chemistry and materials science. |
| Year(s) Of Engagement Activity | 2017 |
| Description | Featured speaker at Cambridge Data Science Summit |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | The Cambridge Data Science Summit is a multi-disciplinary event for researchers, developers and data professionals. Join us for inspiring keynotes, panel debates, practical data science training and numerous networking opportunities to connect, develop new skills and gain insight into the constantly evolving field of data science.Gain new knowledge and insights from a range of sectors, from research and bioinformatics to business and finance. Network and connect with researchers, data scientists and technologists from different domains and industries. |
| Year(s) Of Engagement Activity | 2017 |
| URL | https://www.cambridgenetwork.co.uk/events/data-science-summit/ |
| Description | Francis Crick Institute IVI Seminar |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Professional Practitioners |
| Results and Impact | Follow up visits |
| Year(s) Of Engagement Activity | 2018 |
| Description | General meeting of the European Women in Mathematics Association, 3-7 September 2018, Graz, Austria. Co-organisers: K. Baur, K. Hess, E. Resmerita and S. Terracini. |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | General Meeting of the European Women in Mathematics Association |
| Year(s) Of Engagement Activity | 2018 |
| URL | https://sites.google.com/site/ewmgm18/ |
| Description | HOPES Seminar |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Virtual talk |
| Year(s) Of Engagement Activity | 2020 |
| Description | Hosting of Forum for Royal College of Radiologists |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | A forum space for the Royal College of Radiologists has recently been launched within the CMIH website, allowing members (and those outside of the RCR) to discuss various radiology related matters. |
| Year(s) Of Engagement Activity | 2017 |
| URL | http://cmih.maths.cam.ac.uk/community/ |
| Description | ICFO L4H Seminar, Spain |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Departmental seminar |
| Year(s) Of Engagement Activity | 2016 |
| Description | IEEE International Symposium on Biomedical Imaging |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | The IEEE International Symposium on Biomedical Imaging (ISBI) is a scientific conference dedicated to mathematical, algorithmic, and computational aspects of biological and biomedical imaging, across all scales of observation. It fosters knowledge transfer among different imaging communities and contributes to an integrative approach to biomedical imaging. |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://biomedicalimaging.org/2021/ |
| Description | IMA Conference on Inverse Problems |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Tuesday 19th - Thursday 21st September 2017 An inverse problem denotes the task of computing an unknown physical quantity from indirect measurements. The corresponding forward problem maps the physical quantity to the measurements. In most realistic situations the solution of the inverse problem is challenging, complicated by incomplete and noisy measurements, as well as non-invertible forward operators which render the inverse problem ill-posed (that is lack of stability and/or uniqueness of solutions). Inverse problems appear in many practical applications in biology, medicine, weather forecasting, chemistry, engineering, physics, to name but a few, and their analysis and solution presents considerable challenges in mathematics and statistics. This conference will bring together mathematicians and statisticians, working on theoretical and numerical aspects of inverse problems, and engineers, physicists and other scientists, working on challenging inverse problem applications. We welcome industrial representatives, doctoral students, early career and established academics working in this field to attend. Conference topics: Imaging Regularisation theory Statistical inverse problems Sampling Data assimilation Inverse problem applications Confirmed Invited Speakers: Dr Marta M. Betcke (University College London) Professor Dan Crisan (Imperial College London) Professor Jari Kaipio (University of Auckland, New Zealand) Professor Dirk Lorenz (TU Braunschweig, Germany) Professor Bill Symes (Rice University) Dr Tanja Tarvainen (University of Eastern Finland) Organising Committee: Carola-Bibiane Schönlieb (Cambridge University) - Chair Cristiana Sebu (Oxford Brookes) - Co-chair Paul Ledger (Swansea University) Bill Lionheart (University of Manchester) Scientific Committee: Simon Arridge (University College London) Martin Burger (University of Münster) Daniela Calvetti (Case Western Reserve University) Paul Childs Barbara Kaltenbacher (University of Klagenfurt) Roland Potthast (University of Reading) Samuli Siltanen (University of Helsinki) |
| Year(s) Of Engagement Activity | 2017 |
| URL | http://www.ima.org.uk/conferences/conferences_calendar/inverse-problems.html |
| Description | IMA Conference on Inverse Problems: From Theory To Application |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | This conference brought together mathematicians and statisticians, working on theoretical and numerical aspects of inverse problems, as well as engineers, physicists, and other scientists, working on challenging inverse problem applications. We welcomed industrial representatives, doctoral students, early career and established academics working in this field to attend. |
| Year(s) Of Engagement Activity | 2019 |
| URL | https://ima.org.uk/11329/2nd-ima-conference-on-inverse-problems-from-theory-to-application/ |
| Description | INTERACT 2021 "Sense, Feel, Design" |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | The theme of INTERACT 2021 "Sense, Feel, Design" highlights the new challenges of interaction design. Technology is today more and more widespread, pervasive and blended in the world we live in. On one side, devices that sense humans' activities have the potential to provide an enriched interaction. On the other side, the user experience can be further enhanced by exploiting multisensorial technologies. Not only the traditional human senses of vision and hearing, but also senses of touch, smell, and taste, as well as emotions are to be taken into account when designing for future interactions. INTERACT 2021 represents the right venue to debate such new challenges. Another hot topic of this edition is Human-AI Interaction, focusing on the design of human-centered intelligent systems. |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://www.interact2021.org/ |
| Description | IoP Optics in Clinical Practice |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | Follow up discussions |
| Year(s) Of Engagement Activity | 2018 |
| Description | KTH Departmental Seminar, Sweden |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Professional Practitioners |
| Results and Impact | Departmental seminar in Medical Physics |
| Year(s) Of Engagement Activity | 2016 |
| Description | Launch Event |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Industry/Business |
| Results and Impact | A Launch event was held to celebrate the start of the centre. Talks from relevant academics and partners were given. Relevant stakeholders, from industry, academia, and the public sector (NHS) attended. The proposed work and aims of the centre were well communicated, leading to increased interest from attendees, and links for future work. Attendees gained an increased understanding of the centre, and ways in which they can benefit from its work or work alongside it. |
| Year(s) Of Engagement Activity | 2016 |
| URL | https://www.turing-gateway.cam.ac.uk/event/tgmw31 |
| Description | Light@Bath Workshop |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Postgraduate students |
| Results and Impact | Participation in sandpit workshop including giving scene setting talk |
| Year(s) Of Engagement Activity | 2019 |
| Description | MFO mini-workshop on Deep Learning and Inverse Problems, 4-10 March 2018, Oberwolfach, Germany. Co-organisers: S. Arridge, M. de Hoop and P. Maass. |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Other audiences |
| Results and Impact | Oberwolfach Workshop on Inverse Problems and deep learning. |
| Year(s) Of Engagement Activity | 2018 |
| URL | https://www.mfo.de/occasion/1810c/www_view |
| Description | Mathematics of deep learning |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Aiming to derive a mathematical foundation of deep learning, this programme addresses theoretical questions in two realms: (1) Theoretical foundations of deep learning independent of a particular application. (2) Theoretical analysis of the potential and the limitations of deep learning for mathematical methodologies, in particular, for inverse problems and partial differential equations. The main goal of this programme is to achieve substantial progress in developing a theoretical foundation of deep learning. For this, the programme will for the first time gather the top experts from various areas of mathematics and of the theory of machine learning, including computer scientists, physicists, and statisticians in one place, initiating collaborations across intra- and interdisciplinary boundaries and thereby generating unprecedented research dynamics. |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://www.newton.ac.uk/event/mdl/ |
| Description | Mathematisches Forschungsinstitut Oberwolfach - Geometric Numerical Integration |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | The topics of the workshop included interactions between geometric numerical integration and numerical partial differential equations; geometric aspects of stochastic differential equations; interaction with optimisation and machine learning; new applications of geometric integration in physics; problems of discrete geometry, integrability, and algebraic aspects. |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://publications.mfo.de/handle/mfo/3860 |
| Description | Mathematisches Forschungsinstitut Oberwolfach - Mini-Workshop: Deep Learning and Inverse Problems |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Machine learning and in particular deep learning offer several data-driven methods to amend the typical shortcomings of purely analytical approaches. The mathematical research on these combined models is presently exploding on the experimental side but still lacking on the theoretical point of view. This workshop addresses the challenge of developing a solid mathematical theory for analyzing deep neural networks for inverse problems. |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://publications.mfo.de/handle/mfo/3633 |
| Description | Model-based learning in imaging |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | SIAM's Annual Meeting provides a broad view of the state of the art in applied mathematics, computational science, and their applications through invited presentation, prize lectures, minisymposia, contributed papers and posters. |
| Year(s) Of Engagement Activity | 2017 |
| URL | https://archive.siam.org/meetings/an17/ |
| Description | Naked Scientists |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | Interviewed live on BBC Radio Cambridgeshire by Naked Scientists about 'using light to fight cancer'; the final programme was then published as a podcast and broadcast internationally |
| Year(s) Of Engagement Activity | 2016 |
| Description | PLUS article What the eye can't see |
| Form Of Engagement Activity | A magazine, newsletter or online publication |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | Pictures play a vital role in our lives. They allow us to discover the world, to understand it and to enjoy it. Our own pair of eyes is a powerful tool, but modern imaging technology goes a lot further, revealing distant galaxies and tiny cells in our bodies. Mathematics is the language that underlies this technology, which is why the Cambridge Image Analysis Group, led by Carola-Bibiane Schönlieb, is at home in the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge. |
| Year(s) Of Engagement Activity | 2016 |
| URL | https://plus.maths.org/content/what-eye-cant-see |
| Description | PLUS magazine Mathematical Moments |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | Mathematical moments: Carola-Bibiane Schönlieb Carola-Bibiane Schönlieb has a fascinating job: she works on the mathematics behind image analysis. It finds application in all sorts of areas, from medical imaging, such as MRI scans, to forest ecology, which sees scientists trying to gain information about forests from pictures taken from the air. In this brief interview Carola tells us why she likes doing maths, recalls some of her favourite mathematical moments, and explains why creativity is essential in mathematics. |
| Year(s) Of Engagement Activity | 2016 |
| URL | https://plus.maths.org/content/mathematical-moments-carola-schonlieb |
| Description | POEMS workshop on Big Data, Multimodality & Dynamic Models in Biomedical Imaging, 9th March 2016, Isaac Newton Institute, Cambridge, UK |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Wednesday 9th March 2016 Background We are currently experiencing many new exciting developments in imaging technology in biology and medicine. New advances in tomographic imaging, such as photoacoustic tomography, electron tomography, multicontrast magnetic resonance tomography (MRT) and combined MR with positron emission tomography (PET), as well as new technology in microscopy such as lightsheet microscopy, only mark the beginning of an era which revolutionises the extent of what we can see. New imaging technology always goes side by side with the need of mathematical models to maximise the information gain from these novel imaging techniques. For instance, previously tomographic imaging and light microscopy were separate imaging modalities, which were difficult to cross correlate. However, rapid development of new imaging hardware (light sheet, polarized PET, MRI), is now opening up new avenues for translational multimodal imaging. These developments are supported by sophisticated and rigorous mathematical models, which enhance the information in one imaging modality with information from another. Aims and Objectives New imaging technologies however, also bring new challenges to be overcome. In electron tomography for example, the limited angle problem is an intrinsic hardware limitation which results in viewpoint angles in which the imaged specimen cannot be resolved. Dynamic imaging techniques produce huge amounts of image data which require reliable and efficient methods for interpretation and analysis. This one day meeting aimed to bring together those working on advances in imaging technology with researchers who investigate new image analysis methods, to help address these challenges. In particular, there was a focus on the following topics: Big data problems and solutions Multimodality Dynamic imaging The workshop facilitated the communication of both current opportunities and challenges of new imaging techniques. It also allowed for the sharing of knowledge on current approaches and solutions of mathematical modelling and analysis approaches, with presentations on industry insights and state-of-the-art mathematical techniques for Big Data Analytics. This event was of interest to participants from the biomedical imaging industry, mathematics, engineering, computer science and physics, as well as biology and medicine. |
| Year(s) Of Engagement Activity | 2016 |
| URL | http://www.turing-gateway.cam.ac.uk/event/tgmw32 |
| Description | Panel member at Women in Data Science event at the Isaac Newton Institute, Cambridge |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | The field of Data Science is booming, yet comparatively few women are entering it. Why? What are the obstacles and opportunities facing them if they do? The path to change is challenging, but there are women out there who can testify that it's possible. A Women in Data Science event is being held at the Isaac Newton Institute on Wednesday 7 December within the New Developments in Data Privacy workshop, part of the current INI programme on Data Linkage and Anonymisation. The event included a Women's Round Table Event and Wine Reception Panelists included: Prof Sheila Bird (Medical Research Council) Prof Cynthia Dwork (Microsoft Research and Harvard) Prof Sofia Olhede (UCL) Dr Carola-Bibiane Schönlieb (Cantab Capital Institute for Mathematics of Information, University of Cambridge, and Alan Turing Institute Faculty Fellow) Prof Natalie Shlomo (University of Manchester) |
| Year(s) Of Engagement Activity | 2016 |
| URL | http://www.newton.ac.uk/node/1273254 |
| Description | Photon 2020 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Virtual talk - plenary |
| Year(s) Of Engagement Activity | 2020 |
| Description | Pint of Science |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Public/other audiences |
| Results and Impact | Delivered a lecture paired with optics demonstrations to illustrate novel endoscopic imaging to the public |
| Year(s) Of Engagement Activity | 2016 |
| Description | Podcasting for Radio Cambridge and the Naked Scientists programme |
| Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | Several interviews about cardiovascular research and heart matters. Widely broadcast (Radio 5, ABC in Australia, podcasted). |
| Year(s) Of Engagement Activity | 2014,2015 |
| URL | http://www.thenakedscientists.com/HTML/interviews/interview/1001368/ |
| Description | Presentation at Faculty of Engineering, University of Melbourne |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Postgraduate students |
| Results and Impact | A one-hour presentation held in the Faculty of Engineering, University of Melbourne. The presentation covered the numerical studies on cardiovascular diseases in Cambridge, at particularly EPSRC Centre for Mathematical and Statistical Analysis of Multimodal Clinical Imaging. There around 60 audiences, mostly academic faculties and research students, but also include clinicians. The talk sparked lots of questions and discussions afterwards, with potential academic collaboration forged. |
| Year(s) Of Engagement Activity | 2017 |
| Description | Quarterly Newsletter |
| Form Of Engagement Activity | A magazine, newsletter or online publication |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Supporters |
| Results and Impact | A quarterly newsletter is sent to all who have signed up on our website (currently just over 100), this gives a large spectrum of engagement, including those in industry, the nhs, academia and beyond. The newsletter led to further followers of or twitter feed, and diagnostics show links to larger articles on our activities, and to future events were accessed via the newsletter. |
| Year(s) Of Engagement Activity | 2016,2017,2018 |
| URL | https://us13.campaign-archive.com/home/?u=b8f3673f56646114bea617691&id=d99002a010 |
| Description | Re-visioning Transport and Health 2019, Workshop and Hackathon |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | In this workshop and hackathon, we assessed the state of the field and discussed how developments can be made to provide the data decision-makers in addressing the questions of health and sustainability for transport systems and the built environment. The workshop brought together experts in computer vision, earth observation, street-level data collection, population health, cities, and transport studies. The hackathon had two streams. The first stream involved improving/ developing algorithms to solve an image recognition problem and the second required interdisciplinary work to propose how to solve a real-world problem in a lower and middle income country with imaging data. |
| Year(s) Of Engagement Activity | 2019 |
| URL | https://sites.google.com/view/transportcam2019/home/workshop |
| Description | SPIE Photonics West 2020 |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Plenary session, two sub-sessions and an educational session delivered. Two sessions chaired. |
| Year(s) Of Engagement Activity | 2020 |
| Description | Sci-Phy-4-Health Seminar |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Postgraduate students |
| Results and Impact | Educational session for PhD students |
| Year(s) Of Engagement Activity | 2019 |
| Description | Seeing more in pictures - open mathematics day for Y12 girls |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Schools |
| Results and Impact | Dr Carola Schönlieb gives an insight into some of the mathematics behind image analysis and its wide-ranging applications in fields ranging from developing cancer therapies to restoring artworks, together with some personal reflections on her own career journey through mathematical study and research. Dr Carola Schönlieb is Reader in Applied and Computational Analysis and Head of Cambridge Image Analysis Group at the Department of Applied Mathematics and Theoretical Physics, University of Cambridge. This talk was originally given to an audience of Y12 girls (aged 16-17) at an event for students considering applying to university to study mathematics. The talk was recorded at the Centre for Mathematical Sciences, University of Cambridge, on 18 April 2016. |
| Year(s) Of Engagement Activity | 2016 |
| URL | https://www.youtube.com/watch?v=9SPN9Ouxx7g&feature=youtu.be |
| Description | Soapbox Science |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Public/other audiences |
| Results and Impact | Stood on a soapbox in Cambridge Market Square explaining the concepts of wavelength and polarization, illustrating how they might be used in diagnosis of cancer. |
| Year(s) Of Engagement Activity | 2016 |
| Description | Software For Computing The Forward Problem of EEG For The Case of A realistic Head Model |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | We used the Pseven Datadvance Platform (https://www.datadvance.net/) to train and build a surrogate model for the case of the Forward Problem Associated with EEG imaging. We then shared the code on Github. Here is a link to the code: https://github.com/parham1976/Surrogate-Model-of-OpenMEEG/tree/master/surrogate_model_dir |
| Year(s) Of Engagement Activity | 2018 |
| URL | https://github.com/parham1976/Surrogate-Model-of-OpenMEEG/tree/master/surrogate_model_dir |
| Description | Southampton Bioengineering Research Seminar, UK |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Professional Practitioners |
| Results and Impact | Local Departmental seminar |
| Year(s) Of Engagement Activity | 2016 |
| Description | Support for Cambridge imaging Festival |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | The Cambridge Imaging Festival is an annual conference looking at imaging research across the broad area within healthcare. The event includes sessions on different imaging application and methodologies across the two days. CMIH directors and co-investigators are involved in presenting and discussing CMIH projects at the event and the CMIH provides a small amount of funding for prizes for data blitz sessions. Unfortunately, this conference was postponed until later in 2021 due to COVID. |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://www.mrc-cbu.cam.ac.uk/conferences/cif2021/ |
| Description | TOPIM TECH Introductory Lecture |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Gave a 2 hour introductory lecture to imaging biomarkers with an interactive activity on technical and biological validation of biomarkers. |
| Year(s) Of Engagement Activity | 2017 |
| Description | Talk at Big Data Showcase event in Cambridge on Seeing More in Images: A Mathematical Perspective |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Industry/Business |
| Results and Impact | Background Mathematics underpins so many things we take for granted - smart phones, weather forecasting, architecture, 3D software, structural engineering, sensing technologies, are just a few examples. Indeed, the fruits of mathematical research - such as the resulting techniques and algorithms, affect the daily lives of everyone and the economic impact of mathematics is already well proven. The University of Cambridge's Centre for Mathematical Sciences (CMS) and Big Data Initiative, in partnership with the Turing Gateway to Mathematics highlighted areas of research and expertise in a showcase event, that took place on Wednesday 20th April 2016 at the Centre for Mathematical Sciences in Wilberforce Road, Cambridge. A wide range of talks were given by leading researchers, highlighting areas of mathematical and Big Data sciences. An exhibition ran during the lunch break and the afternoon session. The day ended with a drinks and networking reception between 5.00-6.00PM. The Showcase presented a great opportunity to see what Cambridge has to offer and better understand the diversity and impact that the research in mathematics and big data at Cambridge can make on business and policy across a wide range of areas. Researchers were on hand to discuss specific areas of maths and big data and there was opportunities for delegates to take tours of the GK Batchelor Fluid Dynamics Laboratory and also see the COSMOS Computer. The exhibition included research groups, industrial case studies posters, the EPSRC Centre for Doctoral Training in Analysis, the new Cantab Capital Institute for the Mathematics of Information and details of project opportunities for industry. Aims and Objectives Subjects of talks included industrial areas such as materials and chemical decontamination, mathematical biology, financial maths, cosmology, communications and social sciences. The Showcase presented an excellent opportunity to bring together scientists from mathematics and other disciplines such as physics, chemistry, engineering etc, with interested parties from industry, government and public sectors. The Showcase gave participants a great opportunity to: Meet leading mathematicians and other scientists involved in state-of-the-art mathematical techniques and methods across multiple areas including Big Data Learn more about the potential of mathematics to help provide solutions to real-world problems Find out how to collaborate and partner with the University through research, projects and studentships Network with senior researchers, industry and Government |
| Year(s) Of Engagement Activity | 2016 |
| URL | http://www.turing-gateway.cam.ac.uk/event/tgmw33 |
| Description | Talk at Newton Institute |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Other audiences |
| Results and Impact | Will talk at the workshop at Isaac Newton Institute: Statistics of geometric features and new data types (STSW02) |
| Year(s) Of Engagement Activity | 2018 |
| Description | Tutorial Organisation. Classic vs Deep Vision. What is Beyond Deep Learning in Computer Vision? |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | In this tutorial, we shall draw attention to a new direction that merges the mathematical benefits of classical vision and the powerful performance of deep learning. We will start by introducing the topic and giving an overview of both classic and deep vision perspectives. We will then present a case study of the single image reflection removal problem in which we share solutions coming from the two perspectives. This shall be followed by a discussion of how failure cases - in both perspectives - are related to the modelling hypothesis, and how these failures motivate the need for combined both perspectives. This shall motivate the question - What Is Beyond Deep Learning In Computer Vision? Some open questions related to the topic will also be discussed in the end. |
| Year(s) Of Engagement Activity | 2015,2018 |
| URL | https://sites.google.com/view/accv2018classicvsdl/home |
| Description | Twitter account |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Other audiences |
| Results and Impact | A twitter account has been set up, which reports on the work of the centre, advertises future events, showcases news items, and creates a social media presence for the centre. Posts seem to provide further sign ups to the website/newsletter (as well as vice versa), and produce more followers and retweets. Currently (March 2018) has 155 followers, from a variety of countries |
| Year(s) Of Engagement Activity | 2016 |
| URL | https://twitter.com/CambridgeCMIH |
| Description | UC Davis Biomedical Engineering Seminar, USA |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Local departmental seminar |
| Year(s) Of Engagement Activity | 2016 |
| Description | UEG week |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | 1. Interviewed by Pan European Networks magazine for Horizon 2020 Projects: Portal resulting in an article entitled 'The cutting edge of cancer care' (2016) 2. Featured on EurekAlert! from AAAS, Medical Device Weekly and IndiaTimes (2016). |
| Year(s) Of Engagement Activity | 2016 |
| Description | USC Departmental Seminar, USA |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Gave a talk to the Translational Imaging Center at USC with approximately 30 attendees, which stimulated discussions and resulted in a collaboration between our centres. |
| Year(s) Of Engagement Activity | 2017 |
| Description | Uncertainty Quantification in the Mathematics of Healthcare workshop |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Postgraduate students |
| Results and Impact | A workshop was jointly hosted with the EPSRC centre for Predictive modelling in healthcare, focused on Uncertainty Quantification in the Mathematics of Healthcare. Attendees reported better links with and awareness of the centre, as well as new contacts to discuss potential projects and further work with. Many attendees requested further similar events to discuss other joint problems in the community of maths of healthcare. |
| Year(s) Of Engagement Activity | 2017 |
| URL | http://cmih.maths.cam.ac.uk/uncertainty-quantification-mathematics-healthcare-workshop/ |
| Description | University of Exeter Physics Colloquium |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Professional Practitioners |
| Results and Impact | Local seminar |
| Year(s) Of Engagement Activity | 2019 |
| Description | Variational models and partial differential equations for mathematical imaging |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Plenary lecture at the Conference on the Numerical Solution of Differential and Differential-Algebraic Equations (NUMDIFF-15). Variational models and partial differential equations for mathematical imaging: Images are a rich source of beautiful mathematical formalism and analysis. Associated mathematical problems arise in functional and non-smooth analysis, the theory and numerical analysis of partial differential equations, harmonic, stochastic and statistical analysis, and optimisation. Starting with a discussion on the intrinsic structure of images and their mathematical representation, in this talk we will learn about variational models for image analysis and their connection to partial differential equations, and go all the way to the challenges of their mathematical analysis as well as the hurdles for solving these - typically non-smooth - models computationally. The talk is furnished with applications of the introduced models to image de-noising, motion estimation and segmentation, as well as their use in biomedical image reconstruction such as it appears in magnetic resonance imaging. |
| Year(s) Of Engagement Activity | 2018 |
| URL | https://sim.mathematik.uni-halle.de/numdiff/Numdiff15/index.html |
| Description | Waterhouse |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | Featured on BBC and Financial Times online, as well as numerous trade news outlets and Cambridge TV |
| Year(s) Of Engagement Activity | 2016 |
| Description | Web blog |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | We maintain a blog on our website providing openly accessible descriptions of each paper that we publish. |
| Year(s) Of Engagement Activity | 2015,2016,2017 |
| URL | http://www.bohndieklab.org/blog/ |
| Description | Winter School: The Mathematics of Imaging 7-11 January, 2019 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Helped to co-organise this Winter school "The mathematics of Imaging", that was held in Paris at the IHP (Institut Henri Poincaré), from January 7 to April 5, 2019. The event included course, practical sessions, flash presentations and posters which created much discussion and hopefully new collaborations. |
| Year(s) Of Engagement Activity | 2019 |
| URL | https://imaging-in-paris.github.io/semester2019/school/ |
| Description | Women in MIUA workshop |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Postgraduate students |
| Results and Impact | The WiMIUA is co-located with the 26th Conference on Medical Image Understanding and Analysis (MIUA 2022). The event provides a unique networking platform for academia and industry including engineers, mathematicians, clinicians and entrepreneurs. The event consists on (e)-poster sessions, invited speakers and social gatherings. |
| Year(s) Of Engagement Activity | 2022 |
| URL | https://www.miua2022.com/WiMIUA |
| Description | Women in Mathematics exhibition |
| Form Of Engagement Activity | A magazine, newsletter or online publication |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Undergraduate students |
| Results and Impact | As part of the European Women in Mathematics exhibition, additional portraits of Cambridge Mathematicians, including Carola-Bibiane Schönlieb, were produced. These were accompanied by interviews in Plus magazine, and a launch event, where Carola-Bibiane Schönlieb spoke, and the portraits were then displayed within the Mathematics building. |
| Year(s) Of Engagement Activity | 2017 |
| URL | http://plus.maths.org/content/women |
| Description | Workshop on Gradient flows: challenges and new directions, ICMS 2018 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Gradient flows: challenges and new directions ICMS, The Bayes Centre, 47 Potterrow, Edinburgh EH8 9BT 10 - 14 September 2018 ORGANISERS Bertram Düring, University of Sussex Carola-Bibiane Schönlieb, University of Cambridge Yves van Gennip, University of Nottingham Marie-Therese Wolfram, University of Warwick |
| Year(s) Of Engagement Activity | 2018 |
| URL | https://www.icms.org.uk/gradientflows.php |
| Description | Workshop on High-dimensional Statistics, Inverse Problems and Convex Analysis, Royal Statistical Society, London, UK, 22 March 2016. |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | This workshop will bring together scientists from the statistics, applied mathematics, signal processing and machine learning communities around the topic of convex analysis and its application to challenging inverse problems. The workshop will feature invited talks by world-leading experts presenting cutting edge research on new theory, methodology, and computer algorithms. We aim to provide a valuable opportunity to network and to foster extensive future interaction between the these disciplines. Co-organiser: M. Pereyra. |
| Year(s) Of Engagement Activity | 2016 |
| URL | http://www.bristol.ac.uk/maths/events/2016/high-dimensional-statistics-inverse-problems-and-convex-a... |
| Description | Workshop on Mathematical imaging with partially unknown models, Jesus College in Cambridge, 20-21 Feb. 2017. |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | The CCIMI and CMIH are pleased to be co-sponsoring the Cambridge - Heriot Watt interdisciplinary data science workshop on "Mathematical imaging with partially unknown models" at Jesus College in Cambridge, 20-21 Feb. 2017. More details on the programme will be confirmed at a later date. The workshop is organised by Marcelo Pereyra (Assistant professor, School of Mathematical and Computer Sciences, Heriot Watt) and Carola-Bibiane Schönlieb (Head of the Cantab Capital Institute for the Mathematics of Information (CCIMI), Cambridge), alongside local organiser Martin Benning (Department of Applied Mathematics and Theoretical Physics, Cambridge) The workshop aims to gather an interdisciplinary group of leading imaging experts from the applied analysis, statistics, and signal processing communities around the topic of "imaging with partially unknown models". The goal is to promote synergy and cross-fertilisation between these communities and set the basis for a multidisciplinary approach to the problem. Mathematical imaging is at the core of modern data science, with important applications in medicine, biology, defense, agriculture and environmental sciences. This active research field studies imaging inverse problems involving the estimation of an unobserved true image from measurements that are noisy, incomplete and resolution-limited. This proposal focuses on an increasingly important and particularly challenging class of imaging inverse problems that, in addition to being ill-posed and ill-conditioned, are further complicated by inaccurate and partial knowledge of the observation system and of the properties of the underlying true image (which are essential to regularise the problem and deliver meaningful estimates). These so-called "semi-blind" and "unsupervised" problems are the focus of significant research efforts across a range of scientific communities, particularly applied analysis, Bayesian statistics, and signal processing, which have recently produced important developments in mathematical theory, methods, models and efficient algorithms. The proposed research workshop will focus on three specific aspects of imaging with partially unknown models that will be key in future methodology: learning models from observed data, model comparison and selection in the absence of ground truth, and robust inference with approximate models. Programme; Monday: 9.30 - 10.20: Gabriel Peyré Coffee break (30 minutes) 10.50 - 11.40: Yves Wiaux 11.40 - 12.30: Samuli Siltanen Lunch & Poster session (12.30 - 14.00). Tuesday: 9.30 - 10.20: Silvia Villa Coffee break (30 minutes) 10.50 - 11.40: Juan Carlos de los Reyes 11.40 - 12.30: John Aston Plenary speakers are: Gabriel Peyré (Université Paris-Dauphine) Silvia Villa (Istituto Italiano di Tecnologia and Massachusetts Institute of Technology) Yves Wiaux (Heriot-Watt University) Juan Carlos de los Reyes (Escuela Nacional Politécnica de Quito) John Aston (University of Cambridge) Samuli Siltanen (University of Helsinki) Invited Participants: Yoann Altmann (Heriot Watt, UK) Martin Benning (University of Cambridge, UK) Natalia Bochkina (University of Edinburgh, UK) Matthias Ehrhardt (University of Cambridge, UK) Teresa Klatzer (Graz University of Technology, Austria) Felix Lucka (University College London, UK) Marcelo Pereyra (Heriot Watt, UK) Carola-Bibiane Schönlieb (CCIMI, University of Cambridge, UK) Registrations: Simon Arridge (UCL, UK) Eva-Maria Brinkmann (University of Münster, Germany) Tatiana Alessandra Bubba (University of Helsinki, Finland) Luca Calatroni (Ecole Polytechnique, France) Veronica Corona (University of Cambridge, UK) Jonathan Dunlop (Schlumberger, UK) Silvia Gazzola (University of Bath, UK) Joanna Grah (University of Cambridge, UK) Abderrahim Halimi (Heriot Watt, UK) Karl Harrison (University of Cambridge, UK) Andreas Hauptmann (University of Helsinki, Finland) Jan Holland (Springer-Verlag) Eugenie Hunsicker (Loughborough University, UK) Abdul Jumaat (University of Liverpool, UK) Markus Juvonen (University of Helsinki) Lukas Lang (University of Cambridge, UK) Nguyet Minh Mach (University of Helsinki) Sebastian Neumayer (University of Cambridge, UK) Simone Parisotto (University of Cambridge, UK) Mihaela Pricop-Jeckstadt (TU-Dresden, Germany) Shannon Seah (University of Cambridge, UK) Ferdia Sherry (University of Cambridge, UK) Megan Wilson (University of Cambridge, UK) Joab Winkler (Sheffield University, UK) Evren Yarman (Schlumberger, UK) |
| Year(s) Of Engagement Activity | 2017 |
| URL | http://www.ccimi.maths.cam.ac.uk/events/cambridge-heriot-watt-interdisciplinary-data-science-worksho... |
| Description | Woudschoten Conference 2021 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | The Woudschoten conference has a long and rich history, dating back to the first edition in 1976, and it has featured many of the great names in numerical analysis and scientific computing. Since its establishment in the early days of `approximation and discretization' - the two themes of the first edition of the conference -- the Woudschoten conference has provided an introduction to and overview of groundbreaking developments in scientific computing and numerical analysis. The conference is attended by essentially all Dutch and Flemish researchers in numerical analysis and scientific computing, from PhD students to full professors, and including industrial researchers. By virtue of its unique format and its informal setting, the conference does not only provide insight and inspiration to the Dutch-Flemish numerical-mathematics community, but it also plays a central role in retaining coherence in the community. |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://wsc.project.cwi.nl/woudschoten-conferences/2021-conference |
| Description | YouTube |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | Post videos relating to our research |
| Year(s) Of Engagement Activity | 2017 |
| URL | https://www.youtube.com/channel/UCgKWlKnnn1jYV_OqqFEFziA |