解决病理图像跨医院差异，提升皮肤癌分类准确率
Enhancing Whole Slide Image Classification through Supervised Contrastive Domain Adaptation
- 基于监督对比学习加入领域适应约束
- 在两个中心的六种皮肤癌图像上准确率显著提升
- 适合医学影像跨中心迁移任务的研究者
组织病理学成像中因染色和数字化协议的院内及院间差异，普遍存在领域偏移现象。本文提出一种新型领域适应方法，应对多中心病理图像的变异问题。通过在监督对比学习基础上引入训练约束，增强模型的领域泛化能力并提高类别间可分性。在来自两个中心的六种皮肤癌全切片图像上进行的域适应与分类实验表明，该方法性能优于仅使用特征提取或染色归一化的方案。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。