用差异最小化提升病理模型跨医院泛化能力
Discrepancy Minimization Improves Cross-Hospital Robustness in Digital Pathology
- 引入局部最大均值差异损失，优化跨院数据分布
- 在多个模型和任务中实现跨医院性能提升
- 适用于有无目标医院数据的两种场景
近年来，病理基础模型（PFMs）在组织病理学任务中快速发展，可支持多种分类器训练。然而其跨医院鲁棒性仍受限：在一所医院训练的分类器在另一家医院测试时性能常下降。本文通过在局部最大均值差异（LMMD）目标下微调PFMs，解决此问题。该方法适用于两种场景：域自适应（有未标注的目标医院数据）和域泛化（完全无目标医院数据）。在图像块与整张切片层面的实验均显示，多种PFM和任务下均有稳定提升。
原文摘要 · Abstract (English)
Pathology foundation models (PFMs) have advanced rapidly in recent years and support training classifiers for a range of histopathology tasks. However, their robustness across hospitals remains limited: performance often degrades when training a classifier on data from one hospital and evaluating it on another target hospital. We address this challenge by fine-tuning PFMs with a local maximum mean discrepancy (LMMD) objective that applies to two settings: domain adaptation, where unlabeled target-hospital data is available, and domain generalization, where target-hospital data is unavailable at all. Experiments at both the patch- and slide-level show consistent improvements across multiple PFMs and tasks.
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