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。

CLASSMix：基于自适应染色分离的对比学习与伪标签在组织病理图像分类中的应用

CLASSMix: Adaptive stain separation-based contrastive learning with  pseudo labeling for histopathological image classification

The rapid identification and accurate diagnosis of breast cancer, known as
the killer of women, have become greatly significant for those patients.
Numerous breast cancer histopathological image classification methods have been
proposed. But they still suffer from two problems. (1) These methods can only
hand high-resolution (HR) images. However, the low-resolution (LR) images are
often collected by the digital slide scanner with limited hardware conditions.
Compared with HR images, LR images often lose some key features like texture,
which deeply affects the accuracy of diagnosis. (2) The existing methods have
fixed receptive fields, so they can not extract and fuse multi-scale features
well for images with different magnification factors. To fill these gaps, we
present a \textbf{S}ingle \textbf{H}istopathological \textbf{I}mage
\textbf{S}uper-\textbf{R}esolution \textbf{C}lassification network (SHISRCNet),
which consists of two modules: Super-Resolution (SR) and Classification (CF)
modules. SR module reconstructs LR images into SR ones. CF module extracts and
fuses the multi-scale features of SR images for classification. In the training
stage, we introduce HR images into the CF module to enhance SHISRCNet's
performance. Finally, through the joint training of these two modules,
super-resolution and classified of LR images are integrated into our model. The
experimental results demonstrate that the effects of our method are close to
the SOTA methods with taking HR images as inputs.

本文介绍了一种新的基于超分辨率和多尺度特征提取的单张组织病理学图像分类网络（SHISRCNet），该网络可以更精确地诊断以低分辨率图像为输入的乳腺癌，具有较高的诊断准确度。