扫描诱导的域偏移损害病理基础模型的鲁棒性
Scanner-Induced Domain Shifts Undermine the Robustness of Pathology Foundation Models
AI总结：
本文研究了扫描诱导的域偏移对病理基础模型鲁棒性的影响，发现现有模型在面对扫描设备变异性时存在显著偏差，需改进嵌入稳定性和校准以提升临床可靠性。
摘要：
病理基础模型（PFMs）已成为计算病理学的核心，旨在为从全切片图像（WSIs）中提取特征提供通用编码器。尽管在基准测试中表现强劲，但PFM对现实世界的技术域偏移，如来自全切片扫描仪设备的变异性，其鲁棒性仍知之甚少。我们系统地评估了14个PFM对扫描诱导变异性鲁棒性，包括最先进的模型、早期自监督模型和一个在自然图像上训练的基线模型。使用一个包含384例乳腺癌WSI的多扫描仪数据集，这些WSI在五种设备上扫描，我们独立地将扫描效应与生物和实验室混杂因素分开。鲁棒性通过互补的无监督嵌入分析和一组临床病理学监督预测任务来评估。我们的结果表明，当前PFMs并不对扫描诱导的域偏移具有不变性。大多数模型在其嵌入空间中编码了明显的扫描特定变异性。虽然AUC通常保持稳定，但这种稳定性掩盖了关键的失败模式：扫描变异性系统性地改变嵌入空间，并影响下游模型预测的校准，导致扫描依赖的偏见，这可能影响临床应用的可靠性。我们进一步表明，鲁棒性并非简单地取决于训练数据规模、模型大小或模型新旧。没有模型提供了可靠的鲁棒性以对抗扫描诱导的变异性。虽然在最多样化的数据上训练的模型，这里由视觉-语言模型表示，似乎在鲁棒性方面具有优势，但它们在下游监督任务中表现不佳。我们得出结论，PFMs的开发和评估需要超越以准确性为中心的基准测试，转向显式的嵌入稳定性和校准评估，以应对现实的获取变异性。
英文摘要：
Pathology foundation models (PFMs) have become central to computational pathology, aiming to offer general encoders for feature extraction from whole-slide images (WSIs). Despite strong benchmark performance, PFM robustness to real-world technical domain shifts, such as variability from whole-slide scanner devices, remains poorly understood. We systematically evaluated the robustness of 14 PFMs to scanner-induced variability, including state-of-the-art models, earlier self-supervised models, and a baseline trained on natural images. Using a multiscanner dataset of 384 breast cancer WSIs scanned on five devices, we isolated scanner effects independently from biological and laboratory confounders. Robustness is assessed via complementary unsupervised embedding analyses and a set of clinicopathological supervised prediction tasks. Our results demonstrate that current PFMs are not invariant to scanner-induced domain shifts. Most models encode pronounced scanner-specific variability in their embedding spaces. While AUC often remains stable, this masks a critical failure mode: scanner variability systematically alters the embedding space and impacts calibration of downstream model predictions, resulting in scanner-dependent bias that can impact reliability in clinical use cases. We further show that robustness is not a simple function of training data scale, model size, or model recency. None of the models provided reliable robustness against scanner-induced variability. While the models trained on the most diverse data, here represented by vision-language models, appear to have an advantage with respect to robustness, they underperformed on downstream supervised tasks. We conclude that development and evaluation of PFMs requires moving beyond accuracy-centric benchmarks toward explicit evaluation and optimisation of embedding stability and calibration under realistic acquisition variability.
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