CLASS-M：基于自适应染色分离对比学习与伪标签的组织病理学图像分类
CLASS-M: Adaptive stain separation-based contrastive learning with pseudo-labeling for histopathological image classification
AI总结：
本文提出半监督块级组织病理学图像分类模型CLASS-M，通过自适应染色分离的对比学习与MixUp伪标签模块，在无需大规模标注数据下实现优于SOTA的性能。
摘要：
组织病理学图像分类是医学图像分析中的一项重要任务。由于易于从病理报告中获取病例级标签，近期的方法通常依赖于弱监督学习。然而，在仅有有限数量病例可用或局部预测准确性至关重要的应用中，块级分类更为可取。另一方面，获取带有局部标签的大规模数据集用于训练是不可行的。在本文中，我们提出了一种半监督的块级组织病理学图像分类模型，名为CLASS-M，它不需要大规模带标签的数据集。CLASS-M由两个主要部分组成：一个对比学习模块，使用通过自适应染色分离过程生成的分离的苏木精和伊红图像；另一个是使用MixUp的伪标签模块。我们在两个透明细胞肾细胞癌数据集上将我们的模型与其他最先进的模型进行了比较。我们证明了我们的CLASS-M模型在两个数据集上均具有最佳性能。我们的代码可在github.com/BzhangURU/Paper_CLASS-M/tree/main获取。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
