  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.
