  Interpretability is essential in medical imaging to ensure that clinicians
can comprehend and trust artificial intelligence models. In this paper, we
propose a novel interpretable approach that combines attribute regularization
of the latent space within the framework of an adversarially trained
variational autoencoder. Comparative experiments on a cardiac MRI dataset
demonstrate the ability of the proposed method to address blurry reconstruction
issues of variational autoencoder methods and improve latent space
interpretability. Additionally, our analysis of a downstream task reveals that
the classification of cardiac disease using the regularized latent space
heavily relies on attribute regularized dimensions, demonstrating great
interpretability by connecting the used attributes for prediction with clinical
observations.
