研究评估6个病理学基础模型的中心偏置传播问题
Do Center Biases Propagate? Robustness of Pathology Foundation Models in Whole-Slide Image Classification
这篇论文测了6个病理学基础模型会不会偷偷学到切片来源医院的痕迹，还提出了新指标 AUCC，做计算病理的人应该看看。
该研究考察病理学基础模型（PFM）在全切片图像（WSI）分类中是否会编码采集中心相关的非生物学信号。研究用 Cramér's V 量化类别与中心的关联强度，在4个数据集和2种 MIL 聚合器上基准测试了6个 PFM。团队提出 AUCC 指标，同时衡量分类性能和随伪相关增强时的性能退化。结果显示中心信息会传播到 WSI 级预测，而 ComBat 谐和化在各数据集上未带来一致的鲁棒性提升。
Do Center Biases Propagate? Robustness of Pathology Foundation Models in Whole-Slide Image Classification
Pathology foundation models (PFMs) have transformed computational pathology through powerful representation learning from histopathological images. PFMs provide rich, discriminative representations for whole slide image (WSI) analysis, enabling tasks such as slide-level classification under multiple instance learning (MIL). However, these representations may also encode non-biological signals associated with acquisition centers, potentially introducing spurious shortcuts into downstream predictions. In this work, we evaluate center-associated robustness in WSI classification using a controlled training setting with increasing class-center correlations quantified by Cramér's V. We benchmark six PFMs across four datasets and two MIL aggregators, while evaluating ComBat as a robustification strategy. We further introduce the Area Under the Cramér's V Curve (AUCC) to jointly capture absolute classification performance and its degradation as spurious correlation increases. Results show that center-related information encoded by PFMs propagates to WSI-level predictions, with robustness depending on both the PFM representation and MIL aggregation strategy. Additionally, ComBat harmonization does not provide consistent robustness gains across datasets.
