Joint probabilistic modelling of images and non-imaging covariates: a causal perspective

Daniel Coelho De Castro
Typical machine-learning tasks in imaging applications involve training a model to predict some target annotation (e.g. a class label) from an image. However, by neglecting valuable side-information that is often available in real-world settings, such models are vulnerable to confounding and may not generalise outside the lab environment. This thesis argues for the integration of causality into image analysis workflows, supported by the development of methodologies for jointly modelling images and non-imaging data, especially for...
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