中心偏差会传播吗？病理学基础模型在全切片图像分类中的鲁棒性
Do Center Biases Propagate? Robustness of Pathology Foundation Models in Whole-Slide Image Classification
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中文总结 AI 辅助
本研究评估病理学基础模型在全切片图像分类中是否传播中心偏差，通过受控实验和AUCC指标发现中心信息会传播且鲁棒性依赖模型与聚合策略，ComBat效果不一致。
中文摘要 AI 辅助
病理学基础模型（PFMs）通过从组织病理学图像中进行强大的表征学习，已经改变了计算病理学领域。PFMs为全切片图像（WSI）分析提供了丰富且具有判别性的表征，使得在多重实例学习（MIL）框架下进行切片级分类等任务成为可能。然而，这些表征也可能编码与采集中心相关的非生物学信号，从而在下游预测中引入虚假捷径。在本研究中，我们通过一个受控的训练设置来评估WSI分类中与中心相关的鲁棒性，该设置通过Cramér's V量化逐渐增强的类别-中心相关性。我们在四个数据集和两种MIL聚合器上对六种PFMs进行了基准测试，同时评估了ComBat作为一种鲁棒化策略的效果。我们进一步引入了Cramér's V曲线下面积（AUCC）指标，以联合捕捉绝对分类性能及其随虚假相关性增加而下降的情况。结果表明，PFMs编码的中心相关信息会传播到WSI级别的预测中，其鲁棒性取决于PFM表征和MIL聚合策略。此外，ComBat协调在不同数据集上并未提供一致的鲁棒性提升。
英文摘要
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.
发表机构
- Universitat Politècnica de València (UPV)（瓦伦西亚理工大学）
- Artikode Intelligence S.L.（Artikode Intelligence 有限公司）
机构由 AI 辅助整理，请以论文原文为准。
