Medical image analysis is a vibrant research area that offers doctors and medical
practitioners invaluable insight and the ability to accurately diagnose and monitor disease …

Deep learning analyses have offered sensitivity leaps in detection of cognition-related
variables from functional MRI (fMRI) measurements of brain responses. Yet, as deep models …

Image synthesis is expected to provide value for the translation of machine learning
methods into clinical practice. Fundamental problems like model robustness, domain …

To make medical datasets accessible without sharing sensitive patient information, we
introduce a novel end-to-end approach for generative de-identification of dynamic medical …

Generative AI has revolutionised visual content editing, empowering users to effortlessly
modify images and videos. However, not all edits are equal. To perform realistic edits in …

Estimating the counterfactual outcome of treatment is essential for decision-making in public
health and clinical science, among others. Often, treatments are administered in a …

The framework of causal models provides a principled approach to causal reasoning,
applied today across many scientific domains. Here we present this framework in the …

Counterfactual inference is a powerful tool, capable of solving challenging problems in high-
profile sectors. To perform counterfactual inference, we require knowledge of the underlying …

Quantifying cause and effect relationships is an important problem in many domains, from
medicine to economics. The gold standard solution to this problem is to conduct a …

Two key questions in cardiac image analysis are to assess the anatomy and motion of the
heart from images; and to understand how they are associated with non-imaging clinical …