Title: Modeling the Biological Pathology Continuum with HSIC-regularized Wasserstein Auto-encoders,
Abstract: A crucial challenge in image-based modeling of biomedical data is to identify
trends and features that separate normality and pathology. In many cases, the
morphology of the imaged object exhibits continuous change as it deviates from
normality, and thus a generative model can be trained to model this
morphological continuum. Moreover, given side information that correlates to
certain trend in morphological change, a latent variable model can be
regularized such that its latent representation reflects this side information.
In this work, we use the Wasserstein Auto-encoder to model this pathology
continuum, and apply the Hilbert-Schmitt Independence Criterion (HSIC) to
enforce dependency between certain latent features and the provided side
information. We experimentally show that the model can provide disentangled and
interpretable latent representations and also generate a continuum of
morphological changes that corresponds to change in the side information.