Histopathological image classification is one of the critical aspects in
medical image analysis. Due to the high expense associated with the labeled
data in model training, semi-supervised learning methods have been proposed to
alleviate the need of extensively labeled datasets. In this work, we propose a
model for semi-supervised classification tasks on digital histopathological
Hematoxylin and Eosin (H&E) images. We call the new model Contrastive Learning
with Adaptive Stain Separation and MixUp (CLASSMix). Our model is formed by two
main parts: contrastive learning between adaptively stain separated Hematoxylin
images and Eosin images, and pseudo labeling using MixUp. We compare our model
with other state-of-the-art models on clear cell renal cell carcinoma (ccRCC)
datasets from our institution and The Cancer Genome Atlas Program (TCGA). We
demonstrate that our CLASSMix model has the best performance on both datasets.
The contributions of different parts in our model are also analyzed.

本研究提出了一种新的半监督分类模型，命名为 Contrastive Learning with Adaptive Stain Separation and MixUp (CLASSMix)，该模型是基于适应性染色分离的苏木精和伊红染色图像的对比学习和混合学习的组合，经在肾细胞癌数据集上的对比分析，证明了该模型在两个数据集上的优越性能。