Generative Interpretability: Application in Disease Subtyping

We present a probabilistic approach to characterize heterogeneous disease in a way that is reflective of disease severity. In many diseases, multiple subtypes of disease present simultaneously in each patient. Generative models provide a flexible and readily explainable framework to discover disease subtypes from imaging data. However, discovering local image descriptors of each subtype in a fully unsupervised way is an illposed problem and may result in loss of valuable information about disease severity. Although supervised approaches, and more recently deep learning methods, have achieved state-ofthe-art performance for predicting clinical variables relevant to diagnosis, interpreting those models is a crucial yet challenging task. In this paper, we propose a method that aims to achieve the best of both worlds, namely we maintain the predictive power of supervised methods and the interpretability of probabilistic methods. Taking advantage of recent progress in deep learning, we propose to incorporate the discriminative information extracted by the predictive model into the posterior distribution over the latent variables of the generative model. Hence, one can view the generative model as a template for interpretation of a discriminative method in a clinically meaningful way. We illustrate an application of this method on a large-scale lung CT study of Chronic Obstructive Pulmonary Disease (COPD), which is a highly heterogeneous disease. As our experiments show, our interpretable model does not compromise the prediction of the relevant clinical variables, unlike purely unsupervised methods. We also show that some of the discovered subtypes are correlated with genetic measurements suggesting that the discovered subtypes characterize the underlying etiology of the disease.