用联邦学习+点变换器，从病理切片预测乳腺癌HER2状态
Summary of Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images
- 基于点变换器处理多中心数据的特征与标签不平衡问题
- 在4个站点2687张切片上达到顶尖性能，2个新站点也表现良好
- 适合需要跨机构协作的医疗影像分析研究者
本研究提出一种基于联邦学习的方法，用于从苏木精-伊红（HE）染色的全幻灯片图像（WSIs）中预测乳腺癌HER2状态，降低检测成本并加速治疗决策。针对多中心数据中存在的标签不平衡与特征表示难题，提出一种点变换器模型，融合动态标签分布、辅助分类器和最远余弦采样策略。大量实验表明，该方法在四个站点（共2687张WSI）上实现领先性能，并在两个未见站点（共229张WSI）展现出强泛化能力。
原文摘要 · Abstract (English)
This study introduces a federated learning-based approach to predict HER2 status from hematoxylin and eosin (HE)-stained whole slide images (WSIs), reducing costs and speeding up treatment decisions. To address label imbalance and feature representation challenges in multisite datasets, a point transformer is proposed, incorporating dynamic label distribution, an auxiliary classifier, and farthest cosine sampling. Extensive experiments demonstrate state-of-the-art performance across four sites (2687 WSIs) and strong generalization to two unseen sites (229 WSIs).
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