好的、坏的和脆弱的：病理基础模型的稳健性和泛化基准测试
The Good, the Bad, and the Brittle: Benchmarking Robustness and Generalisation of Histopathology Foundation Models
AI总结：
用REET工具和NR-Kfold协议对12个病理基础模型及ResNet基线进行基准测试，引入PPI分析，表明病理基础模型在稳健性和泛化上优于CNNs，模型扩展收益递减，强调需评估分布变化。
摘要：
病理基础模型的稳健性和泛化能力如何，其扩展极限是否已达到？我们使用鲁棒性评估和增强工具箱（REET）以及基于差异驱动的非冗余K折验证（NR-Kfold）协议，对12个病理基础模型（PFMs）和ResNet基线进行了跨11种临床现实扰动的基准测试。我们引入了扰动性能指数（PPI）来总结在受控扰动扫描下的准确性趋势，并分析参数数量与鲁棒性扩展的关系。我们表明，PFMs在稳健性和领域泛化方面始终优于CNNs，但模型扩展显示出收益递减：中型模型（如UNI2/Virchow-2等）比大型系统具有相当或更高的弹性。NR-Kfold分析进一步揭示了训练-测试相似性被打破时系统准确性的损失和变异性的增加，强调了明确分布转移评估的必要性。这些发现表明，下一代病理基础模型必须优先考虑数据质量、多模态信息和领域对齐，而不是参数数量，以实现真正的临床可靠性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
