Learning to Generalize towards Unseen Domains via a Content-Aware Style Invariant Framework for Disease Detection from Chest X-rays
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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.
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