Learning disentangled representations for explainable chest X-ray classification using Dirichlet VAEs

TL;DR

This paper demonstrates that Dirichlet VAEs can learn disentangled, interpretable features from chest X-ray images, improving explainability and marginally enhancing classification performance compared to Gaussian VAEs.

Contribution

The study introduces the use of Dirichlet VAEs for disentangled representation learning in CXR classification, enabling explainability through gradient-guided latent traversals.

Findings

DirVAE disentangles class-specific visual features.

DirVAE achieves marginally better classification accuracy.

Explainability method highlights clinically relevant regions.

Abstract

This study explores the use of the Dirichlet Variational Autoencoder (DirVAE) for learning disentangled latent representations of chest X-ray (CXR) images. Our working hypothesis is that distributional sparsity, as facilitated by the Dirichlet prior, will encourage disentangled feature learning for the complex task of multi-label classification of CXR images. The DirVAE is trained using CXR images from the CheXpert database, and the predictive capacity of multi-modal latent representations learned by DirVAE models is investigated through implementation of an auxiliary multi-label classification task, with a view to enforce separation of latent factors according to class-specific features. The predictive performance and explainability of the latent space learned using the DirVAE were quantitatively and qualitatively assessed, respectively, and compared with a standard Gaussian prior-VAE (GVAE). We introduce a new approach for explainable multi-label classification in which we conduct gradient-guided latent traversals for each class of interest. Study findings indicate that the DirVAE is able to disentangle latent factors into class-specific visual features, a property not afforded by the GVAE, and achieve a marginal increase in predictive performance relative to GVAE. We generate visual examples to show that our explainability method, when applied to the trained DirVAE, is able to highlight regions in CXR images that are clinically relevant to the class(es) of interest and additionally, can identify cases where classification relies on spurious feature correlations.