In histopathology, pathologists examine both tissue architecture at low magnification and fine-grained morphology at high magnification. Yet, the performance of pathology foundation models across magnifications and the effect of magnification sampling during training remain poorly understood. We model magnification sampling as a multi-source domain adaptation problem and develop a simple theoretical framework that reveals systematic trade-offs between sampling strategies. We show that the widely used discrete uniform sampling of magnifications (0.25, 0.5, 1.0, 2.0 mpp) leads to degradation at intermediate magnifications. We introduce continuous magnification sampling, which removes gaps in magnification coverage while preserving performance at standard scales. Further, we derive sampling distributions that optimize representation quality across magnification scales. To evaluate these strategies, we introduce two new benchmarks (TCGA-MS, BRACS-MS) with appropriate metrics. Our experiments show that continuous sampling substantially improves over discrete sampling at intermediate magnifications, with gains of up to 4 percentage points in balanced classification accuracy, and that optimized distributions can further improve performance. Finally, we evaluate current histopathology foundation models, finding that magnification is a primary driver of performance variation across models. Our work paves the way towards future pathology foundation models that perform reliably across magnifications.

翻译：在组织病理学中，病理学家需要同时观察低倍镜下的组织结构和高倍镜下的精细形态。然而，病理学基础模型在不同放大倍率下的性能表现，以及训练过程中放大倍率采样的影响，目前仍缺乏深入理解。本文将放大倍率采样建模为一个多源域适应问题，并建立了一个简洁的理论框架，揭示了不同采样策略间存在的系统性权衡。研究表明，广泛使用的离散均匀放大倍率采样方法（0.25、0.5、1.0、2.0 mpp）会导致中间倍率下的性能退化。我们提出了连续放大倍率采样方法，该方法在消除放大倍率覆盖间隙的同时，保持了标准尺度下的性能表现。此外，我们推导出了能够优化跨放大倍率尺度表征质量的采样分布。为评估这些策略，我们引入了两个包含相应评估指标的新基准测试（TCGA-MS、BRACS-MS）。实验结果表明，在中间放大倍率下，连续采样方法相较于离散采样方法有显著提升，在平衡分类准确率上最高可获得4个百分点的增益，且优化后的分布能进一步提升性能。最后，我们对当前的组织病理学基础模型进行了评估，发现放大倍率是导致模型间性能差异的主要因素。本研究为未来开发能在不同放大倍率下均保持可靠性能的病理学基础模型奠定了基础。