对比学习能有效降低域间差异，提升医学图像分类效果。
Bridging Contrastive Learning and Domain Adaptation: Theoretical Perspective and Practical Application
- 用理论证明对比损失可缩小类间最大差异度
- 在三种乳腺影像数据上分类准确率显著提升
- 适合医疗图像领域的自监督域适应任务
本文从理论角度研究对比学习与域适应的关系。两种标准对比损失——NT-Xent（自监督）和有监督对比损失——均与广泛用于域适应的类间均值最大差异度（CMMD）相关。研究表明，最小化对比损失可降低CMMD并增强类别可分性，为对比学习在域适应中的应用提供了理论基础。鉴于域适应在医学影像中的重要性，实验聚焦于乳腺影像。在三个乳腺影像数据集——合成图像块、临床真实图像块和临床真实图像——上的大量实验表明，采用有监督对比损失可同时提升域适应能力、类别可分性和分类性能。
原文摘要 · Abstract (English)
This work studies the relationship between Contrastive Learning and Domain Adaptation from a theoretical perspective. The two standard contrastive losses, NT-Xent loss (Self-supervised) and Supervised Contrastive loss, are related to the Class-wise Mean Maximum Discrepancy (CMMD), a dissimilarity measure widely used for Domain Adaptation. Our work shows that minimizing the contrastive losses decreases the CMMD and simultaneously improves class-separability, laying the theoretical groundwork for the use of Contrastive Learning in the context of Domain Adaptation. Due to the relevance of Domain Adaptation in medical imaging, we focused the experiments on mammography images. Extensive experiments on three mammography datasets - synthetic patches, clinical (real) patches, and clinical (real) images - show improved Domain Adaptation, class-separability, and classification performance, when minimizing the Supervised Contrastive loss.
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