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：组织病理学图像分类是医学图像分析中的重要任务。由于从病理报告中获取病例级标签较为容易，现有方法通常依赖于弱监督学习。然而，在病例数量有限或局部预测精度至关重要的应用场景中，斑块级分类更为可取。另一方面，获取具有局部标注的大规模数据集进行训练并不可行。本文提出了一种半监督斑块级组织病理学图像分类模型，命名为CLASS-M，该模型无需大量标注数据集。CLASS-M由两个主要部分组成：一个对比学习模块，利用通过自适应染色分离过程生成的分离苏木精和伊红图像；以及一个采用MixUp的伪标记模块。我们在两个透明细胞肾细胞癌数据集上将所提模型与其他先进模型进行比较。实验结果表明，我们的CLASS-M模型在两个数据集上均取得最佳性能。代码发布于github.com/BzhangURU/Paper_CLASS-M/tree/main