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.

翻译：深度生成模型已成为数据生成与操控的重要工具。通过选择性修改数据属性来增强这些模型的可控性，是近期研究的重点。变分自编码器在捕捉隐藏属性方面展现出潜力，但常产生模糊的重建结果。在医学影像中，通过不同成像域控制这些属性颇具挑战。近期，软内省变分自编码器通过将对抗性损失融入VAE训练，结合了VAE与生成对抗网络的优点，展现出卓越的图像合成能力。本研究提出属性软内省变分自编码器（Attri-SIVAE），通过在Soft-Intro VAE框架中引入属性正则化损失。我们利用来自不同领域（如不同扫描仪厂商和采集中心）的心脏MRI数据，对所提方法进行实验评估。与当前最优的属性正则化VAE相比，该方法在重建和正则化方面实现了相似性能，且更关键的是，在测试不同数据集时能维持相同的正则化水平，而对比方法则无法做到。