  Interpretability is essential in medical imaging to ensure that clinicians
can comprehend and trust artificial intelligence models. Several approaches
have been recently considered to encode attributes in the latent space to
enhance its interpretability. Notably, attribute regularization aims to encode
a set of attributes along the dimensions of a latent representation. However,
this approach is based on Variational AutoEncoder and suffers from blurry
reconstruction. In this paper, we propose an Attributed-regularized Soft
Introspective Variational Autoencoder that combines attribute regularization of
the latent space within the framework of an adversarially trained variational
autoencoder. We demonstrate on short-axis cardiac Magnetic Resonance images of
the UK Biobank the ability of the proposed method to address blurry
reconstruction issues of variational autoencoder methods while preserving the
latent space interpretability.
