Histopathological image classification is an important task in medical image analysis. Recent approaches generally rely on weakly supervised learning due to the ease of acquiring case-level labels from pathology reports. However, patch-level classification is preferable in applications where only a limited number of cases are available or when local prediction accuracy is critical. On the other hand, acquiring extensive datasets with localized labels for training is not feasible. In this paper, we propose a semi-supervised patch-level histopathological image classification model, named CLASS-M, that does not require extensively labeled datasets. CLASS-M is formed by two main parts: a contrastive learning module that uses separated Hematoxylin and Eosin images generated through an adaptive stain separation process, and a module with pseudo-labels using MixUp. We compare our model with other state-of-the-art models on two clear cell renal cell carcinoma datasets. We demonstrate that our CLASS-M model has the best performance on both datasets. Our code is available at github.com/BzhangURU/Paper_CLASS-M/tree/main

翻译：组织病理图像分类是医学图像分析中的一项重要任务。由于从病理报告中获取病例级标签较为便捷，近期方法通常依赖弱监督学习。然而，在可用病例数量有限或局部预测精度至关重要的应用中，补丁级分类更为可取。另一方面，获取带有局部标签的大规模数据集用于训练并不可行。本文提出一种名为CLASS-M的半监督补丁级组织病理图像分类模型，该模型无需大规模标注数据集。CLASS-M由两个主要部分组成：一是通过自适应染色分离过程生成的苏木精和伊红分离图像进行对比学习的模块，二是使用MixUp生成伪标签的模块。我们在两个透明细胞肾细胞癌数据集上将该模型与其他现有最优模型进行比较，证明CLASS-M模型在两个数据集上均取得了最佳性能。我们的代码已发布于github.com/BzhangURU/Paper_CLASS-M/tree/main。