ArXiv Preprint
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.
Mohammad Zunaed, Md. Aynal Haque, Taufiq Hasan
2023-02-27
