ConDiSR: Contrastive Disentanglement and Style Regularization for Single
  Domain Generalization
Aleksandr Matsun Numan Saeed Fadillah Adamsyah Maani Mohammad Yaqub
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% in average accuracy while also
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