通过最优传输与多实例学习，仅用少量样本即可稳定控制病理图像分类模型的敏感度。
Robust sensitivity control in digital pathology via tile score distribution matching
- 基于最优传输和多实例学习，实现对全切片图像分类模型敏感度的精准调控。
- 在多个队列和任务上验证，仅需少量校准样本即可实现稳定敏感度控制。
- 适合需要满足临床敏感度合规要求的数字病理系统部署场景。
在不同医疗中心部署数字病理模型面临分布偏移的挑战。尽管领域泛化方法能提升模型整体性能（以受试者工作特征曲线下面积AUC衡量），但临床监管常要求控制其他指标，如预设的敏感度水平。本文提出一种新方法，基于最优传输与多实例学习，实现对全切片图像（WSI）分类模型敏感度的控制。在多个队列和任务中验证，该方法仅需少量校准样本即可实现稳健的敏感度调控，为计算病理系统的可靠部署提供实用解决方案。
原文摘要 · Abstract (English)
Deploying digital pathology models across medical centers is challenging due to distribution shifts. Recent advances in domain generalization improve model transferability in terms of aggregated performance measured by the Area Under Curve (AUC). However, clinical regulations often require to control the transferability of other metrics, such as prescribed sensitivity levels. We introduce a novel approach to control the sensitivity of whole slide image (WSI) classification models, based on optimal transport and Multiple Instance Learning (MIL). Validated across multiple cohorts and tasks, our method enables robust sensitivity control with only a handful of calibration samples, providing a practical solution for reliable deployment of computational pathology systems.
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