Attribute Regularized Soft Introspective VAE: Towards Cardiac Attribute Regularization Through MRI Domains
This work addresses the challenge of attribute controllability in medical imaging for applications like cardiac MRI analysis, but it is incremental as it builds on existing Soft Introspective VAE frameworks.
The paper tackled the problem of controlling attributes in deep generative models for medical imaging by proposing the Attributed Soft Introspective VAE, which achieved similar reconstruction and regularization performance as state-of-the-art methods while maintaining regularization levels across different cardiac MRI datasets.
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.