Self-supervised learning has become a cornerstone in various areas, particularly histopathological image analysis. Image augmentation plays a crucial role in self-supervised 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 SRA-MoCo v3. We demonstrate that our SRA-MoCo v3 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.

翻译：自监督学习已成为多个领域，尤其是组织病理学图像分析的基石。图像增强在自监督学习中发挥着关键作用，因为它能生成图像样本的变体。然而，传统的图像增强技术常常忽视组织病理学图像的独特特性。本文提出了一种新的、针对组织病理学的图像增强方法，称为染色重建增强（SRA）。我们将SRA与自监督对比学习中的领先模型MoCo v3以及我们额外的对比损失项相结合，并将新模型命名为SRA-MoCo v3。我们证明，在各种下游任务中，我们的SRA-MoCo v3始终优于标准MoCo v3，并且与在规模大得多的组织病理学数据集上预训练的其他基础模型相比，取得了相当或更优的性能。