Self-supervised contrastive learning has become a cornerstone in various areas, particularly histopathological image analysis. Image augmentation plays a crucial role in self-supervised contrastive learning, as it generates variations in image samples. However, traditional image augmentation techniques often overlook the unique characteristics of histopathological images. In this paper, we propose a new histopathology-specific image augmentation method called stain reconstruction augmentation (SRA). We integrate our SRA with MoCo v3, a leading model in self-supervised contrastive learning, along with our additional contrastive loss terms, and call the new model PathMoCo. We demonstrate that our PathMoCo always outperforms the standard MoCo v3 across various downstream tasks and achieves comparable or superior performance to other foundation models pre-trained on significantly larger histopathology datasets.

本研究针对传统图像增强技术未能充分考虑病理图像的独特特性这一问题，提出了一种名为染色重建增强（SRA）的新型病理图像增强方法。通过将SRA与MoCo v3模型结合并引入额外的对比损失项，命名为PathMoCo，我们的实验结果表明PathMoCo在各项下游任务中均优于标准MoCo v3，并且在一些情况下性能可与在更大病理数据集上预训练的其他基础模型相媲美。

PathMoCo：一种新颖的框架以改善自监督对比学习中病理图像的特征嵌入