  We propose a hierarchically structured variational inference model for
accurately disentangling observable evidence of disease (e.g. brain lesions or
atrophy) from subject-specific anatomy in brain MRIs. With flexible, partially
autoregressive priors, our model (1) addresses the subtle and fine-grained
dependencies that typically exist between anatomical and pathological
generating factors of an MRI to ensure the clinical validity of generated
samples; (2) preserves and disentangles finer pathological details pertaining
to a patient's disease state. Additionally, we experiment with an alternative
training configuration where we provide supervision to a subset of latent
units. It is shown that (1) a partially supervised latent space achieves a
higher degree of disentanglement between evidence of disease and
subject-specific anatomy; (2) when the prior is formulated with an
autoregressive structure, knowledge from the supervision can propagate to the
unsupervised latent units, resulting in more informative latent representations
capable of modelling anatomy-pathology interdependencies.
