Domain shift in the field of histopathological imaging is a common phenomenon due to the intra- and inter-hospital variability of staining and digitization protocols. The implementation of robust models, capable of creating generalized domains, represents a need to be solved. In this work, a new domain adaptation method to deal with the variability between histopathological images from multiple centers is presented. In particular, our method adds a training constraint to the supervised contrastive learning approach to achieve domain adaptation and improve inter-class separability. Experiments performed on domain adaptation and classification of whole-slide images of six skin cancer subtypes from two centers demonstrate the method's usefulness. The results reflect superior performance compared to not using domain adaptation after feature extraction or staining normalization.

本研究针对由于医院间染色和数字化协议差异导致的组织病理影像领域转移问题，提出了一种新的领域适应方法，旨在提高模型的通用性。通过在监督对比学习中引入训练约束，该方法显著改善了类间可分离性，实验结果表明，在进行六种皮肤癌亚型的全切片图像分类时，应用该方法的性能优于不进行领域适应的传统方法。

通过监督对比领域适应增强全切片图像分类