  Deep learning (DL) methods where interpretability is intrinsically considered
as part of the model are required to better understand the relationship of
clinical and imaging-based attributes with DL outcomes, thus facilitating their
use in the reasoning behind medical decisions. Latent space representations
built with variational autoencoders (VAE) do not ensure individual control of
data attributes. Attribute-based methods enforcing attribute disentanglement
have been proposed in the literature for classical computer vision tasks in
benchmark data. In this paper, we propose a VAE approach, the Attri-VAE, that
includes an attribute regularization term to associate clinical and medical
imaging attributes with different regularized dimensions in the generated
latent space, enabling a better-disentangled interpretation of the attributes.
Furthermore, the generated attention maps explained the attribute encoding in
the regularized latent space dimensions. Using the Attri-VAE approach we
analyzed healthy and myocardial infarction patients with clinical, cardiac
morphology, and radiomics attributes. The proposed model provided an excellent
trade-off between reconstruction fidelity, disentanglement, and
interpretability, outperforming state-of-the-art VAE approaches according to
several quantitative metrics. The resulting latent space allowed the generation
of realistic synthetic data in the trajectory between two distinct input
samples or along a specific attribute dimension to better interpret changes
between different cardiac conditions.
