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：源域不匹配导致的性能退化是深学习医学图像分析的长期挑战,特别是在胸前X光方面。为了应对这一领域的变化,已经提出了几种方法,例如利用对抗性学习或多多面混杂来提取域变量高层次特征。然而,这些方法并未明确说明或规范所提取域变量的内容和风格属性。最近的研究显示,CNN模型表现出强烈偏向风格(即,纹理)而不是内容,这与人造系统形成鲜明的对比。对于对医学领域图像的强有力和普遍理解来说,解释性表示至关重要。因此,所学的高层次语义特征既需要针对内容的区分,例如:病理特异性和域异异性,以及风格。受此启发,我们提出了一个新颖的框架,通过更多关注内容,减少风格偏差,来改进跨多面性性能。我们在图像和地貌水平上采用一个风格随机式随机随机随机随机随机随机随机随机随机化模块来创建透视流流化的模板。我们用Oralalalalalalalalalalalal-deal-deal-dealdeal ex-deal-deal-deal-deal-deal-listanstanstanstandeal-liflifildeal ex-liflistal ex-liversal ex-wiaus ex-wistal-wical-wistal-wistal-wical-wifus ex-widismex-wimamamamatous-wimato-to-wimatototototototototototototo-widal-widal-widismex-wi,我们用一个Ofildex-widal-to-to-widal-lifildex-lial-s-lial-lidal-to-to-i-li-to-to-to-to-totototo-lial-lial-lial-lial-widex-to-to-to-to-to-to-to-to-to-to-to-to-i-to-to-to-to-totomamamas-to-to-to-to-to-ial-ial-to</s>