Performance degradation due to source domain mismatch is a longstanding
challenge in deep learning-based medical image analysis, particularly for chest
X-rays. Several methods have been proposed to address this domain shift, such
as utilizing adversarial learning or multi-domain mixups to extract
domain-invariant high-level features. However, these methods do not explicitly
account for or regularize the content and style attributes of the extracted
domain-invariant features. Recent studies have demonstrated that CNN models
exhibit a strong bias toward styles (i.e., textures) rather than content, in
stark contrast to the human-vision system. Explainable representations are
paramount for a robust and generalizable understanding of medical images. Thus,
the learned high-level semantic features need to be both content-specific,
i.e., pathology-specific and domain-agnostic, as well as style invariant.
Inspired by this, we propose a novel framework that improves cross-domain
performances by focusing more on content while reducing style bias. We employ a
style randomization module at both image and feature levels to create stylized
perturbation features while preserving the content using an end-to-end
framework. We extract the global features from the backbone model for the same
chest X-ray with and without style randomized. We apply content consistency
regularization between them to tweak the framework's sensitivity toward content
markers for accurate predictions. Extensive experiments on unseen domain test
datasets demonstrate that our proposed pipeline is more robust in the presence
of domain shifts and achieves state-of-the-art performance. Our code is
available via
this https URL.

提出了一种针对医疗图像分析中源域不匹配问题的新框架，该框架采用风格随机化模块和内容一致性正则化，旨在提取既与病理特异性有关、同时又具有域不可知特性的高级语义特征。在未经训练的测试数据集上的广泛实验表明，该方法更加健壮，达到了最先进的性能水平。