ConDiSR: Contrastive Disentanglement and Style Regularization for Single Domain Generalization
arxiv(2024)
摘要
Medical data often exhibits distribution shifts, which cause test-time
performance degradation for deep learning models trained using standard
supervised learning pipelines. This challenge is addressed in the field of
Domain Generalization (DG) with the sub-field of Single Domain Generalization
(SDG) being specifically interesting due to the privacy- or logistics-related
issues often associated with medical data. Existing disentanglement-based SDG
methods heavily rely on structural information embedded in segmentation masks,
however classification labels do not provide such dense information. This work
introduces a novel SDG method aimed at medical image classification that
leverages channel-wise contrastive disentanglement. It is further enhanced with
reconstruction-based style regularization to ensure extraction of distinct
style and structure feature representations. We evaluate our method on the
complex task of multicenter histopathology image classification, comparing it
against state-of-the-art (SOTA) SDG baselines. Results demonstrate that our
method surpasses the SOTA by a margin of 1
showing more stable performance. This study highlights the importance and
challenges of exploring SDG frameworks in the context of the classification
task. The code is publicly available at
https://github.com/BioMedIA-MBZUAI/ConDiSR
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