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Chapter and Conference Paper
Learning Task-Specific and Shared Representations in Medical Imaging
The performance of multi-task learning hinges on the design of feature sharing between tasks; a process which is combinatorial in the network depth and task count. Hand-crafting an architecture based on human...
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Chapter and Conference Paper
Uncertainty in Multitask Learning: Joint Representations for Probabilistic MR-only Radiotherapy Planning
Multi-task neural network architectures provide a mechanism that jointly integrates information from distinct sources. It is ideal in the context of MR-only radiotherapy planning as it can jointly regress a sy...
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Chapter and Conference Paper
Manifold Learning of COPD
Analysis of CT scans for studying Chronic Obstructive Pulmonary Disease (COPD) is generally limited to mean scores of disease extent. However, the evolution of local pulmonary damage may vary between patients ...
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Chapter and Conference Paper
Multi-scale Analysis of Imaging Features and Its Use in the Study of COPD Exacerbation Susceptible Phenotypes
We propose a novel framework for exploring patterns of respiratory pathophysiology from paired breath-hold CT scans. This is designed to enable analysis of large datasets with the view of determining relations...
