This study explores the use of the Dirichlet Variational Autoencoder (DirVAE) for learning disentangled latent representations of chest X-ray (CXR) images. Our working hypothesis is that distributional sparsity, as facilitated by the Dirichlet prior, will encourage disentangled feature learning for the complex task of multi-label classification of CXR images. The DirVAE is trained using CXR images from the CheXpert database, and the predictive capacity of multi-modal latent representations learned by DirVAE models is investigated through implementation of an auxiliary multi-label classification task, with a view to enforce separation of latent factors according to class-specific features. The predictive performance and explainability of the latent space learned using the DirVAE were quantitatively and qualitatively assessed, respectively, and compared with a standard Gaussian prior-VAE (GVAE). We introduce a new approach for explainable multi-label classification in which we conduct gradient-guided latent traversals for each class of interest. Study findings indicate that the DirVAE is able to disentangle latent factors into class-specific visual features, a property not afforded by the GVAE, and achieve a marginal increase in predictive performance relative to GVAE. We generate visual examples to show that our explainability method, when applied to the trained DirVAE, is able to highlight regions in CXR images that are clinically relevant to the class(es) of interest and additionally, can identify cases where classification relies on spurious feature correlations.

翻译：本研究探讨了狄利克雷变分自编码器（DirVAE）在学习胸部X光（CXR）图像解耦潜在表征中的应用。我们的工作假设是，狄利克雷先验所促进的分布稀疏性将有助于CXR图像多标签分类复杂任务中的解耦特征学习。DirVAE使用CheXpert数据库中的CXR图像进行训练，并通过实施辅助多标签分类任务来探究DirVAE模型学习到的多模态潜在表征的预测能力，旨在根据类别特异性特征增强潜在因子的分离。采用定量与定性方法分别评估DirVAE学习到的潜在空间的预测性能与可解释性，并与标准高斯先验变分自编码器（GVAE）进行对比。我们提出了一种新的可解释性多标签分类方法，该方法针对每个目标类别进行梯度引导的潜在空间遍历。研究结果表明，DirVAE能够将潜在因子解耦为类别特异性的视觉特征（这是GVAE不具备的特性），并在预测性能上相比GVAE实现了小幅提升。我们通过生成可视化示例证明，当将可解释性方法应用于训练后的DirVAE时，该方法不仅能突出CXR图像中与目标类别临床相关的区域，还能识别分类依赖于虚假特征关联的案例。