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

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1) Brief description of the context of the research including potential impactApplying 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 MethodologyThis 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 areasThis 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 involvedNot applicable.