Learning Interpretable representation in medical applications is becoming essential for adopting data-driven models into clinical practice. It has been recently shown that learning a disentangled feature representation is important for a more compact and explainable representation of the data. In this paper, we introduce a novel adversarial variational autoencoder with a total correlation constraint to enforce independence on the latent representation while preserving the reconstruction fidelity. Our proposed method is validated on a publicly available dataset showing that the learned disentangled representation is not only interpretable, but also superior to the state-of-the-art methods. We report a relative improvement of 81.50% in terms of disentanglement, 11.60% in clustering, and 2% in supervised classification with a few amounts of labeled data.
@article{arxiv.1904.08491,
title = {Learning Interpretable Disentangled Representations using Adversarial VAEs},
author = {Mhd Hasan Sarhan and Abouzar Eslami and Nassir Navab and Shadi Albarqouni},
journal= {arXiv preprint arXiv:1904.08491},
year = {2019}
}