Cambridge Mathematics of Information in Healthcare (CMIH)
Lead Research Organisation:
University of Cambridge
Department Name: Applied Maths and Theoretical Physics
Abstract
In our work in the current edition of the CMIH we have built up a strong pool of researchers and collaborations across the board from mathematics, statistics, to engineering, medical physics and clinicians. Our work has also confirmed that imaging data is a very important diagnostic biomarker, but also that non-imaging data in the form of health records, memory tests and genomics are precious predictive resources and that when combined in appropriate ways should be the source for AI-based healthcare of the future.
Following this philosophy, the new CMIH brings together researchers from mathematics, statistics, computer science and medicine, with clinicians and relevant industrial stakeholder to develop rigorous and clinically practical algorithms for analysing healthcare data in an integrated fashion for personalised diagnosis and treatment, as well as target identification and validation on a population level. We will focus on three medical streams: Cancer, Cardiovascular disease and Dementia, which remain the top 3 causes of death and disability in the UK.
Whilst applied mathematics and mathematical statistics are still commonly regarded as separate disciplines there is an increasing understanding that a combined approach, by removing historic disciplinary boundaries, is the only way forward. This is especially the case when addressing methodological challenges in data science using multi-modal data streams, such as the research we will undertake at the Hub. This holistic approach will support the Hub aims to bring AI for healthcare decision making to the clinical end users.
Following this philosophy, the new CMIH brings together researchers from mathematics, statistics, computer science and medicine, with clinicians and relevant industrial stakeholder to develop rigorous and clinically practical algorithms for analysing healthcare data in an integrated fashion for personalised diagnosis and treatment, as well as target identification and validation on a population level. We will focus on three medical streams: Cancer, Cardiovascular disease and Dementia, which remain the top 3 causes of death and disability in the UK.
Whilst applied mathematics and mathematical statistics are still commonly regarded as separate disciplines there is an increasing understanding that a combined approach, by removing historic disciplinary boundaries, is the only way forward. This is especially the case when addressing methodological challenges in data science using multi-modal data streams, such as the research we will undertake at the Hub. This holistic approach will support the Hub aims to bring AI for healthcare decision making to the clinical end users.
Planned Impact
The interdisciplinary approach relating fundamental mathematical research to the life sciences is desired but rarely delivered. Our experience in the current Cambridge EPSRC maths in healthcare centre has taught us that the main reason for this is the considerable effort needed to deeply engage with researchers in other disciplines outside of one's own. Genuinely impactful applied mathematical work needs strong relationships and trust between investigators. The CMIH already has a strong track-record of delivery with > 100 papers (approximately 50/50 in mathematical and medical journals and > 1000 citations), >10 medical image analysis software packages, 30 active interdisciplinary and industrial collaborations, and >£18M of further research funding. We thus believe that our new Hub will start from a position of great strength to develop clinically-purposeful algorithms (in close collaboration with clinicians) and to turn those into clinically practical AI tools (the latter will be supported by the Dundee HDR UK arm, i.e. Emily Jefferson).
Beyond academic beneficiaries, the hub will focus on using this advantageous position to deliver impact in healthcare areas. The location of the hub lends itself to benefit from the numerous related activities already happening within Cambridge, where interdisciplinary biomedical research is second to none. Engaging with partners like Cancer Research UK Cambridge Institute, the Wellcome Trust Genome Campus or the NIHR Cambridge Biomedical Research Centre significantly increases the likelihood of tangible impact being delivered at the hub. Furthermore, we will work with the other EPSRC funded hubs to maximise the joint impact provided to healthcare users and the general public more widely.
Developments in healthcare technologies have far reaching impacts beyond the academic research alone. Improvements in clinical decision making processes through the integration of multi-modal data and AI will advance patient treatments and care, thus improving outcomes while reducing costs. Beneficiaries include the clinicians themselves and the healthcare organisations they represent, the NHS (and other equivalent health services internationally), patients and ultimately the general public. Furthermore, the IP generated from the hub and the subsequent technological innovation will benefit technology companies and the wider healthcare tech community, further improving the ability of healthcare providers to diagnose, treat and care for patients and their families.
Beyond academic beneficiaries, the hub will focus on using this advantageous position to deliver impact in healthcare areas. The location of the hub lends itself to benefit from the numerous related activities already happening within Cambridge, where interdisciplinary biomedical research is second to none. Engaging with partners like Cancer Research UK Cambridge Institute, the Wellcome Trust Genome Campus or the NIHR Cambridge Biomedical Research Centre significantly increases the likelihood of tangible impact being delivered at the hub. Furthermore, we will work with the other EPSRC funded hubs to maximise the joint impact provided to healthcare users and the general public more widely.
Developments in healthcare technologies have far reaching impacts beyond the academic research alone. Improvements in clinical decision making processes through the integration of multi-modal data and AI will advance patient treatments and care, thus improving outcomes while reducing costs. Beneficiaries include the clinicians themselves and the healthcare organisations they represent, the NHS (and other equivalent health services internationally), patients and ultimately the general public. Furthermore, the IP generated from the hub and the subsequent technological innovation will benefit technology companies and the wider healthcare tech community, further improving the ability of healthcare providers to diagnose, treat and care for patients and their families.
Organisations
- University of Cambridge (Lead Research Organisation)
- Cancer Research UK Cambridge Institute (Collaboration)
- Royal Institute of Technology (Collaboration)
- Dassault Systèmes (United Kingdom) (Project Partner)
- Aviva Plc (Project Partner)
- Siemens (United Kingdom) (Project Partner)
- General Electric (United Kingdom) (Project Partner)
- GlaxoSmithKline (United Kingdom) (Project Partner)
- National Physical Laboratory (Project Partner)
- Canon Medical Research Europe Ltd (Project Partner)
- AstraZeneca (United Kingdom) (Project Partner)
- Cambridgeshire & Peterborough NHS FT (Project Partner)
- The Alan Turing Institute (Project Partner)
- Feedback Medical (Project Partner)
Publications
Tokgoz A
(2022)
Association of Collagen, Elastin, Glycosaminoglycans, and Macrophages With Tissue Ultimate Material Strength and Stretch in Human Thoracic Aortic Aneurysms: A Uniaxial Tension Study.
in Journal of biomechanical engineering
Toner YC
(2022)
Systematically evaluating DOTATATE and FDG as PET immuno-imaging tracers of cardiovascular inflammation.
in Scientific reports
Tsampasian V
(2022)
Management of asymptomatic severe aortic stenosis: a systematic review and meta-analysis.
in Open heart
Tzolos E
(2021)
18F-Sodium fluoride PET/CT detects transcatheter aortic valve degeneration
in European Heart Journal - Cardiovascular Imaging
Utukuri M
(2022)
Digital health: a neglected part of health curricula?
in Future healthcare journal
Vaghari D
(2023)
Validating the clinical utility of AI-guided tools for early dementia prediction
in Alzheimer's & Dementia
Van Eijnatten M
(2021)
3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning.
in Computer methods and programs in biomedicine
Wall C
(2021)
Pericoronary and periaortic adipose tissue density are associated with inflammatory disease activity in Takayasu arteritis and atherosclerosis.
in European heart journal open
Wall C
(2023)
CT pericoronary adipose tissue density predicts coronary allograft vasculopathy and adverse clinical outcomes after cardiac transplantation
in European Heart Journal
Wall C
(2024)
CT Pericoronary Adipose Tissue Density Predicts Coronary Allograft Vasculopathy And Adverse Clinical Outcomes After Cardiac Transplantation
in Journal of Cardiovascular Computed Tomography
Wang J
(2022)
MIR99AHG inhibits EMT in pulmonary fibrosis via the miR-136-5p/USP4/ACE2 axis.
in Journal of translational medicine
Wei K.
(2022)
TFPnP: Tuning-free Plug-and-Play Proximal Algorithms with Applications to Inverse Imaging Problems
in Journal of Machine Learning Research
Wei Kaixuan
(2022)
TFPnP: Tuning-free Plug-and-Play Proximal Algorithms with Applications to Inverse Imaging Problems
in JOURNAL OF MACHINE LEARNING RESEARCH
Wei T
(2022)
Beyond fine-tuning: Classifying high resolution mammograms using function-preserving transformations.
in Medical image analysis
Wei Y
(2023)
Multi-Modal Learning for Predicting the Genotype of Glioma
in IEEE Transactions on Medical Imaging
Wei Y
(2023)
Structural connectome quantifies tumour invasion and predicts survival in glioblastoma patients.
in Brain : a journal of neurology
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
Weir-McCall JR
(2023)
Reply: First-Line Coronary CT Angiography in Chronic Coronary Syndrome: An Internationally Oriented Translational Outlook.
in JACC. Cardiovascular imaging
Welsh SJ
(2022)
Dynamic biomarker and imaging changes from a phase II study of pre- and post-surgical sunitinib.
in BJU international
Wibmer AG
(2021)
Oncologic Outcomes after Localized Prostate Cancer Treatment: Associations with Pretreatment Prostate Magnetic Resonance Imaging Findings.
in The Journal of urology
Woitek R
(2021)
Hyperpolarized Carbon-13 MRI for Early Response Assessment of Neoadjuvant Chemotherapy in Breast Cancer Patients.
in Cancer research
Yan G
(2022)
Prognostic significance of MRI-based late-course tumor volume in locoregionally advanced nasopharyngeal carcinoma.
in Radiation oncology (London, England)
Yang Yijun
(2023)
MammoDG: Generalisable Deep Learning Breaks the Limits of Cross-Domain Multi-Center Breast Cancer Screening
in arXiv e-prints
Yang Yijun
(2023)
Video Adverse-Weather-Component Suppression Network via Weather Messenger and Adversarial Backpropagation
in arXiv e-prints
Yang Yijun
(2024)
Genuine Knowledge from Practice: Diffusion Test-Time Adaptation for Video Adverse Weather Removal
in arXiv e-prints
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
Zhao T
(2022)
Regulatory T-Cell Response to Low-Dose Interleukin-2 in Ischemic Heart Disease
in NEJM Evidence
Zhao T
(2020)
Low dose interleukin-2 in patients with stable ischaemic heart disease and acute coronary syndrome (LILACS)
in European Heart Journal
Description | AI-guided precision diagnosis in dementia |
Amount | £233,726 (GBP) |
Funding ID | INF\R2\202107 |
Organisation | The Royal Society |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 01/2021 |
End | 12/2024 |
Description | Building Global AI-guided Digital Solutions for Brain and Mental health |
Amount | £580,760 (GBP) |
Organisation | Alan Turing Institute |
Sector | Academic/University |
Country | United Kingdom |
Start | 09/2021 |
End | 03/2023 |
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 | 05/2021 |
End | 05/2026 |
Description | Optimising and translating AI to improve prognosis and clinical pathways |
Amount | £749,299 (GBP) |
Organisation | Wellcome Trust |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 02/2022 |
End | 01/2025 |
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 | 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 | 12/2023 |
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 | 01/2022 |
End | 08/2027 |
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 | 09/2020 |
End | 09/2023 |
Title | Research Data Supporting "Modelling prognostic trajectories of cognitive decline due to Alzheimer's disease" |
Description | |
Type Of Material | Database/Collection of data |
Year Produced | 2020 |
Provided To Others? | Yes |
URL | https://www.repository.cam.ac.uk/handle/1810/301740 |
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 | 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 | 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 | Alan Turing Institute and Joint Biosecurity Center |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Policymakers/politicians |
Results and Impact | Discussing issues with the current machine learning literature for COVID-19 and giving recommedations. |
Year(s) Of Engagement Activity | 2021 |
URL | https://www.turing.ac.uk/research/research-projects/new-partnership-between-alan-turing-institute-an... |
Description | AstraZeneca Data Science Jamboree Plenary |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | A high level introduction to the application of AI to COVID-19 imaging and clinical data and developments in the AIX-COVNET / BloodCounts! consortia. |
Year(s) Of Engagement Activity | 2021 |
Description | BBC interview with Pallab Ghosh |
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 | BBC interview with Pallab Ghosh on AI-tools for early dementia detection, July 2021. https://www.bbc.co.uk/news/health-57934589 https://www.cam.ac.uk/stories/AIdementia https://www.turing.ac.uk/news/ai-could-detect-dementia-after-single-brain-scan |
Year(s) Of Engagement Activity | 2021 |
URL | https://www.bbc.co.uk/news/health-57934589 |
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 | 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 | 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 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 | Cambridge Imaging Festival 2021 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Professional Practitioners |
Results and Impact | Discussions about AI for COVID-19 and our tool for modelling outcomes [note that I spoke in place of Carola]. |
Year(s) Of Engagement Activity | 2021 |
URL | https://www.mrc-cbu.cam.ac.uk/wp-content/uploads/2021/05/Programme-Cambridge-Imaging-Festival-2021.p... |
Description | Chair and Speaker AI for Mental Health |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | AI for Mental Health, AI UK, London UK, Chair and speaker |
Year(s) Of Engagement Activity | 2021 |
Description | Chair and speaker CogX |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Chair and speaker at Data Science for Mental Health, CogX, London UK, June 2019 |
Year(s) Of Engagement Activity | 2019 |
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 | 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 | ERS presentation |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Presentation discussing abstract for IPF tracking using airway volumes. |
Year(s) Of Engagement Activity | 2021 |
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 | 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 | 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/IPEM 2021 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Presentation describing the work of AIX-COVNET and BloodCounts! |
Year(s) Of Engagement Activity | 2021 |
URL | https://www.youtube.com/watch?v=sm8i1SrV97w |
Description | Invited speaker AIUK meeting |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | AIUK meeting: AI for Mental Health |
Year(s) Of Engagement Activity | 2021 |
Description | Invited speaker ARUK conference |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Invited speaker ARUK conference: EDoN Initiative |
Year(s) Of Engagement Activity | 2021 |
Description | Invited speaker AstraZeneca |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Invited speaker at AstraZeneca Data Science Event |
Year(s) Of Engagement Activity | 2021 |
Description | Invited speaker HDR UK Scientific conference |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | HDR UK Scientific conference: AI for Neurodegenerative Diseases |
Year(s) Of Engagement Activity | 2021 |
Description | Invited speaker Royal Society |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Professional Practitioners |
Results and Impact | Invited speaker at Royal Society, Meeting of Minds |
Year(s) Of Engagement Activity | 2021 |
Description | Invited speaker World Alzheimer Report 2021 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Invited speaker Alzheimer's disease International: Innovations in diagnosis and diagnostics - World Alzheimer Report 2021, October 2021 |
Year(s) Of Engagement Activity | 2021 |
Description | Launch Event of the Cambridge Mathematics of Information in Healthcare (CMIH) |
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 was the first external event of the CMIH Hub and aimed to bring together those working in mathematical healthcare data analytics across the UK, including academic, clinical, and industrial users with mathematicians working in similar areas. The event included talks that highlighted open challenges and successes from CMIH Hub researchers and presented other potential collaborative opportunities, as well as projects being developed elsewhere related to healthcare data analytics. Talks focused on the key theme of the CMIH Hub, which is the development of robust and clinical applicable algorithms for analysing healthcare data in an integrated fashion. In addition, there were introductory talks from partner EPSRC Hubs for Mathematical Sciences in Healthcare. This event brought together researchers from mathematics, statistics, computer science and medicine, with clinicians and relevant industrial stakeholders. |
Year(s) Of Engagement Activity | 2021 |
URL | https://gateway.newton.ac.uk/event/tgmw93 |
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 | 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 | Discussions at a workshop highlighting issues with AI in imaging and for clinical data. |
Year(s) Of Engagement Activity | 2021 |
URL | https://physical-reasoning.github.io/industry/ |
Description | Presentation at Alzheimer's Association International Conference |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Presentation 'Predicting future regional tau accumulation in preclinical Alzheimer's disease.' Joseph Giorgio, William Jagust, Suzanne Baker, Susan Landau, Peter Tino and Zoe Kourtzi. |
Year(s) Of Engagement Activity | 2020 |
Description | Seminar given at the University of Warwick |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Other audiences |
Results and Impact | I presented my work on Flexible Krylov methods for inverse problems in the (virtual) applied maths seminar at Warwick. |
Year(s) Of Engagement Activity | 2021 |
Description | World demential council roundtable |
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 | World demential council: roundtable discussion on technology and dementia, London, UK, March 2021 |
Year(s) Of Engagement Activity | 2021 |
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 |