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We propose a hierarchically structured variational inference model foraccurately disentangling observable evidence of disease (e.g. brain lesions oratrophy) from subject-specific anatomy in brain MRIs. With flexible, partiallyautoregressive priors, our model (1) addresses the subtle and fine-graineddependencies that typically exist between anatomical and pathologicalgenerating factors of an MRI to ensure the clinical validity of generatedsamples; (2) preserves and disentangles finer pathological details pertainingto a patient's disease state. Additionally, we experiment with an alternativetraining configuration where we provide supervision to a subset of latentunits. It is shown that (1) a partially supervised latent space achieves ahigher degree of disentanglement between evidence of disease andsubject-specific anatomy; (2) when the prior is formulated with anautoregressive structure, knowledge from the supervision can propagate to theunsupervised latent units, resulting in more informative latent representationscapable of modelling anatomy-pathology interdependencies.

Clinically Plausible Pathology-Anatomy Disentanglement in Patient Brain MRI with Structured Variational Priors