Interpretability is essential in medical imaging to ensure that clinicians can comprehend and trust artificial intelligence models. Several approaches have been recently considered to encode attributes in the latent space to enhance its interpretability. Notably, attribute regularization aims to encode a set of attributes along the dimensions of a latent representation. However, this approach is based on Variational AutoEncoder and suffers from blurry reconstruction. In this paper, we propose an Attributed-regularized Soft Introspective Variational Autoencoder that combines attribute regularization of the latent space within the framework of an adversarially trained variational autoencoder. We demonstrate on short-axis cardiac Magnetic Resonance images of the UK Biobank the ability of the proposed method to address blurry reconstruction issues of variational autoencoder methods while preserving the latent space interpretability.

翻译：在医学影像领域，可解释性对于确保临床医生能够理解并信任人工智能模型至关重要。近期已有多种方法尝试在潜在空间中编码属性以增强其可解释性。值得注意的是，属性正则化方法旨在沿着潜在表征的维度编码一组属性。然而，该方法基于变分自编码器，存在重建图像模糊的问题。本文提出一种属性正则化软自省变分自编码器，将潜在空间的属性正则化与对抗训练变分自编码器框架相结合。我们在英国生物银行的短轴心脏磁共振图像上验证了所提方法的能力，既能解决变分自编码器重建模糊的问题，又能保持潜在空间的可解释性。