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 https://github.com/rafizunaed/domain_agnostic_content_aware_style_invariant.

翻译：由于源域不匹配导致的性能退化是深度学习医学图像分析中长期存在的挑战，尤其是在胸片分析领域。研究者已提出多种方法来应对这种域偏移，例如利用对抗学习或多域混合策略提取域不变的高层特征。然而，这些方法并未显式地考虑或约束所提取域不变特征的内容与风格属性。最新研究表明，CNN模型对风格（即纹理）存在显著偏好，而非内容特征，这与人类视觉系统形成鲜明对比。可解释性表征对于稳健且可泛化的医学图像理解至关重要，因此所学习的高层语义特征需同时具备内容特异性（即病理特异性与域无关性）和风格不变性。受此启发，我们提出一种新型框架，通过聚焦内容特征并减少风格偏差来提升跨域性能。该框架在图像层和特征层均采用风格随机化模块，在保持内容特征的同时生成风格扰动特征，并通过端到端架构实现。对同一胸片分别进行有/无风格随机化处理后，我们从骨干模型提取全局特征，通过施加内容一致性正则化来调整框架对内容标记的敏感度以实现精准预测。在未见域测试数据集上的大量实验表明，我们所提出的管道在域偏移场景中具有更强的鲁棒性，并达到了当前最优性能。相关代码已开源至 https://github.com/rafizunaed/domain_agnostic_content_aware_style_invariant。