针对病理图像特点设计新增强方法，提升自监督学习效果
SRA: A Novel Method to Improve Feature Embedding in Self-supervised Learning for Histopathological Images
- 提出专为病理图像设计的染色重建增强法
- 在多个下游任务中均优于标准MoCo v3模型
- 适合做病理图像分析的自监督学习研究者
自监督学习已成为病理图像分析的重要基础。图像增强在自监督学习中起关键作用，能生成图像样本的多样化变体。然而，传统增强方法常忽略病理图像的独特特性。本文提出一种新型病理图像专用增强方法——染色重建增强（SRA），并将其与MoCo v3结合，加入额外对比损失项，构建新模型SRA-MoCo v3。实验表明，SRA-MoCo v3在多种下游任务中始终优于标准MoCo v3，并达到或超越在更大规模病理数据集上预训练的其他基线模型性能。
原文摘要 · Abstract (English)
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.
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