Not-So-Supervised Meta-Learning for Medical Image Analysis

Lead Research Organisation: University College London
Department Name: Medical Physics and Biomedical Eng

Abstract

1) Brief description of the context of the research including potential impact

Applying modern deep learning methodologies in medical imaging applications has shown potentials to automate and to further improve a wide range of clinical applications such as anatomy recognition, localization and pathology quantification. However, requiring large amount of expert labelled training data, uncertainties associated with the labels and difficulties in interpreting the predictions have hindered the clinical adoption of these approaches. This project investigates a meta-learning strategy that can generalise the benefits from weakly-supervised, semi-supervised and unsupervised techniques to imaging applications for prostate cancer and lung disease diagnosis, overcoming these limitations.

2) Aims and Objectives
-The specific objectives are to:

This project aims to develop novel meta-learning methods to address the generalisation issue in deep-learning-based medical image analysis, in particular, incorporating weak supervision, semi-supervision and unsupervised algorithms in meta-learning to minimize the dependency on subjective clinical labelling.

The proposed research will focus on learning underlying supervision signals together with intrinsic data structures in both task- and meta-optimization stages. It is the central hypothesis in this project that, by combining the two complementary perspectives of learning strategies, medical image analysis application will benefit the most: while meta-learning utilises its potential to learn "prior" knowledge between tasks, the not-so-supervised algorithms learn the "inner" data knowledge between the "inner-loop data". Not only to overcome the limitation on labelled data, a more interesting direction is to quantify the representation, learned to improve small-data training, to discover the reasoning of the predictions or link these predictions to clinically interpretable measurements.

3) Novelty of Research Methodology

This project plans to investigate the roles of weak supervision, semi-supervision and unsupervised algorithms in meta-learning, exploring underlying supervision signals in two nested loops. In the inner loop, "pseudo-labels" learned from unsupervised methods will be used to enhance the supervision, improving the performance for target tasks. In the outer loop, task similarities will be investigated using metric learning, improving the generalization to target tasks. Moreover, correlation analysis will be used to quantify the data structure and similarity to known interpretable knowledge, such as clinical measurements and label quality. This helps to measure model and data uncertainties and interpret machine predictions, such as radiologist annotations and scores.

4) Alignment to EPSRC's strategies and research areas
This project aligns with EPSRC's 'healthcare technologies' theme and 'artificial intelligence technologies' research area. Through this project, we aim to address the 'frontiers of physical intervention' challenge that has been laid out as part of the healthcare technologies theme strategy. The project will also address identified challenges in enabling minimally invasive procedures through medical image-based planning and guidance and potential the related research topic includes physical modelling and its applicability in computer aided intervention.

5) Any companies or collaborators involved
Not applicable.

Planned Impact

The critical mass of scientists and engineers that i4health will produce will ensure the UK's continued standing as a world-leader in medical imaging and healthcare technology research. In addition to continued academic excellence, they will further support a future culture of industry and entrepreneurship in healthcare technologies driven by highly trained engineers with deep understanding of the key factors involved in delivering effective translatable and marketable technology. They will achieve this through high quality engineering and imaging science, a broad view of other relevant technological areas, the ability to pinpoint clinical gaps and needs, consideration of clinical user requirements, and patient considerations. Our graduates will provide the drive, determination and enthusiasm to build future UK industry in this vital area via start-ups and spin-outs adding to the burgeoning community of healthcare-related SMEs in London and the rest of the UK. The training in entrepreneurship, coupled with the vibrant environment we are developing for this topic via unique linkage of Engineering and Medicine at UCL, is specifically designed to foster such outcomes. These same innovative leaders will bolster the UK's presence in medical multinationals - pharmaceutical companies, scanner manufacturers, etc. - and ensure the UK's competitiveness as a location for future R&D and medical engineering. They will also provide an invaluable source of expertise for the future NHS and other healthcare-delivery services enabling rapid translation and uptake of the latest imaging and healthcare technologies at the clinical front line. The ultimate impact will be on people and patients, both in the UK and internationally, who will benefit from the increased knowledge of health and disease, as well as better treatment and healthcare management provided by the future technologies our trainees will produce.

In addition to impact in healthcare research, development, and capability, the CDT will have major impact on the students we will attract and train. We will provide our talented cohorts of students with the skills required to lead academic research in this area, to lead industrial development and to make a significant impact as advocates of the science and engineering of their discipline. The i4health CDT's combination of the highest academic standards of research with excellent in-depth training in core skills will mean that our cohorts of students will be in great demand placing them in a powerful position to sculpt their own careers, have major impact within our discipline, while influencing the international mindset and direction. Strong evidence demonstrates this in our existing cohorts of students through high levels of conference podium talks in the most prestigious venues in our field, conference prizes, high impact publications in both engineering, clinical, and general science journals, as well as post-PhD fellowships and career progression. The content and training innovations we propose in i4health will ensure this continues and expands over the next decade.

Publications

10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S021930/1 01/10/2019 31/03/2028
2578103 Studentship EP/S021930/1 01/10/2021 30/09/2025 Qi Li