基于物理的对抗性染色增强与校准覆盖保证
Physics-Grounded Adversarial Stain Augmentation with Calibrated Coverage Guarantees
AI总结：
本文提出CASA方法，在Macenko染色参数空间中通过DKW不等式校准预算进行对抗性增强，提升多中心部署下的病理模型准确率。
摘要：
跨医院的染色差异会降低病理模型在部署时的性能。现有增强方法通过任意超参数扰动颜色空间，缺乏原理化的预算和对未见中心的覆盖保证。我们提出CASA，通过DKW不等式从多中心统计中校准预算，在Camelyon17-WILDS（5种子）上实现93.9%±1.6%的滑片级准确率，优于HED-strong、RandStainNA和ERM方法，且在所有10种比较方法中具有最高的最差组准确率。
英文摘要：
Stain variation across hospitals degrades histopathology models at deployment. Existing augmentation methods perturb color spaces with arbitrary hyperparameters, lacking both a principled budget and coverage guarantees for unseen centers. We propose \textbf{C}alibrated \textbf{A}dversarial \textbf{S}tain \textbf{A}ugmentation (\textbf{CASA}), which performs adversarial augmentation in the Macenko stain parameter space with a budget calibrated from multi-center statistics via the DKW inequality. On Camelyon17-WILDS (5 seeds), CASA achieves $93.9\% \pm 1.6\%$ slide-level accuracy -- outperforming HED-strong ($88.4\% \pm 7.3\%$), RandStainNA ($85.2\% \pm 6.7\%$), and ERM ($63.9\% \pm 11.3\%$) -- with the highest worst-group accuracy ($84.9\% \pm 0.9\%$) among all 10 compared methods.
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。
