SRA：一种改进组织病理图像自监督学习中特征嵌入的新方法
SRA: A Novel Method to Improve Feature Embedding in Self-supervised Learning for Histopathological Images
AI总结：
针对传统图像增强未适配组织病理图像特性的问题，提出染色重建增强SRA方法，结合MoCo v3与额外对比损失构建SRA-MoCo v3模型，在下游任务中性能优于标准MoCo v3，可媲美甚至超越更大数据集预训练的基础模型。
摘要：
自监督学习已成为诸多领域尤其是组织病理图像分析领域的核心支撑技术。图像增强在自监督学习中至关重要，它能为图像样本生成多样化变体。然而，传统图像增强技术往往忽略了组织病理图像的独特特性。本文提出了一种全新的组织病理专属图像增强方法，名为染色重建增强（stain reconstruction augmentation，SRA）。我们将SRA与自监督对比学习领域的主流模型MoCo v3相结合，同时加入我们提出的额外对比损失项，将新模型命名为SRA-MoCo v3。实验表明，在各类下游任务中，SRA-MoCo v3的性能始终优于标准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，其性能相当甚至更优。
英文摘要：
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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