对比12个病理模型，发现中等规模模型更抗干扰且泛化更强。
The Good, the Bad, and the Brittle: Benchmarking Robustness and Generalisation of Histopathology Foundation Models
- 用11种临床扰动测试模型鲁棒性，引入性能指数量化结果
- 中等参数量模型（如UNI2）比大模型更稳定，规模扩大收益递减
- 强调数据质量与多模态融合比堆参数更重要，适合临床应用研究者
我们使用鲁棒性评估与增强工具箱（REET）对12个病理基础模型（PFMs）和ResNet基线进行了评估，覆盖十一类临床真实扰动，并采用非冗余交叉验证（NR-Kfold）协议。提出扰动性能指数（PPI）以总结扰动下的准确率变化趋势，分析模型规模与鲁棒性的关系。结果表明，PFMs在鲁棒性和领域泛化上持续优于传统CNN，但模型规模扩大后收益递减：中等规模模型（如UNI2/Virchow-2）表现不逊于甚至优于大型模型。NR-Kfold分析显示，当训练与测试数据分布差异增大时，准确率系统性下降且波动加剧，凸显分布偏移评估的必要性。研究建议下一代病理基础模型应更注重数据质量、多模态信息与领域对齐，而非单纯增加参数量，以实现真正的临床可靠性。
原文摘要 · Abstract (English)
How robust and generalisable are pathology foundation models and have their scaling limites been reached? We benchmarked twelve pathology foundation models (PFMs) and ResNet baselines using our Robustness Evaluation and Enhancement Toolbox (REET) across eleven clinically realistic perturbations and a dissimilarity-driven Non-Redundant K-fold validation (NR-Kfold) protocol. We introduce a Perturbation Performance Index (PPI) to summarise accuracy trends under controlled perturbation sweeps and analyse robustness scaling with parameter count. We show that PFMs consistently outperform CNNs in both robustness and domain generalisation, yet model scaling shows diminishing returns: mid-sized models such (UNI2/Virchow-2 etc.) achieve comparable or greater resilience than larger systems. NR-Kfold analysis further reveals systematic accuracy loss and increased variability when training-test similarity is broken, underscoring the need for explicit distribution-shift evaluation. These findings suggest that the next generation of pathology foundation models must prioritise data quality, multimodality information and domain alignment over parameter count to achieve genuine clinical reliability.
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