Deep generative models have emerged as influential instruments for data
generation and manipulation. Enhancing the controllability of these models by
selectively modifying data attributes has been a recent focus. Variational
Autoencoders (VAEs) have shown promise in capturing hidden attributes but often
produce blurry reconstructions. Controlling these attributes through different
imaging domains is difficult in medical imaging. Recently, Soft Introspective
VAE leverage the benefits of both VAEs and Generative Adversarial Networks
(GANs), which have demonstrated impressive image synthesis capabilities, by
incorporating an adversarial loss into VAE training. In this work, we propose
the Attributed Soft Introspective VAE (Attri-SIVAE) by incorporating an
attribute regularized loss, into the Soft-Intro VAE framework. We evaluate
experimentally the proposed method on cardiac MRI data from different domains,
such as various scanner vendors and acquisition centers. The proposed method
achieves similar performance in terms of reconstruction and regularization
compared to the state-of-the-art Attributed regularized VAE but additionally
also succeeds in keeping the same regularization level when tested on a
different dataset, unlike the compared method