Modeling the Biological Pathology Continuum with HSIC-regularized Wasserstein Auto-encoders
This work addresses the problem of interpreting pathological trends in biomedical imaging for researchers, but it is incremental as it combines existing methods (Wasserstein Auto-encoders and HSIC) for a specific application.
The paper tackled the challenge of modeling continuous morphological changes from normality to pathology in biomedical images by developing a generative model that uses Wasserstein Auto-encoders regularized with HSIC to align latent features with side information, resulting in disentangled and interpretable representations that generate a continuum of changes corresponding to the side information.
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.