用病理基础模型和细胞级可解释性，从常规染色切片中快速精准识别生物标志物。
Towards Cellular-Scale Interpretability in Pathology Foundation Models for Biomarker Assessment
- 基于8万张全切片图像训练模型，聚焦生物标志物检测。
- 在10项任务中5项排名第一，整体性能优于现有模型。
- 细胞级解释模块获病理医生青睐，可发现疑难病例错误模式。
病理学中的分子生物标志物检测通常成本高且耗材多，限制了其大规模临床应用。将人工智能应用于苏木精-伊红（HE）染色组织切片，可实现快速生物标志物筛查，但临床转化需兼具高精度与可解释性。本文提出Hireca——一个在来自三家医疗中心、涵盖38个器官类型的超过8万张全切片图像上预训练的生物标志物导向病理基础模型，搭配CytoMap可解释性模块，用于定位预测背后的细胞级证据。在涵盖形态、分子、遗传及空间转录组代理读数的10项生物标志物任务中，Hireca在5项任务中排名第一，整体表现优于同类模型。由来自两国的八位病理医生评估显示，CytoMap始终优于其他可视化方法，并揭示了复杂病例中的错误模式。结果表明，Hireca与CytoMap构成了一套可临床审查的、直接从常规HE切片进行生物标志物评估的透明框架。
原文摘要 · Abstract (English)
Molecular biomarker testing in pathology is often costly and tissue-consuming, limiting scalable clinical deployment. Artificial intelligence applied to hematoxylin and eosin (HE)-stained histology could enable rapid biomarker screening, but clinical translation requires models that are both accurate and interpretable. Here we introduce Hireca, a biomarker-focused pathology foundation model pretrained on more than 80,000 whole-slide images spanning 38 organ types from three medical centers, together with CytoMap, an interpretability module that localizes cellular-scale evidence underlying predictions. Across 10 biomarker tasks encompassing morphological, molecular, genetic, and spatial-transcriptomic-proxy readouts, Hireca ranked first in five tasks and outperformed comparable models overall. In evaluation by eight pathologists from two countries, CytoMap was consistently preferred over alternative visualization approaches and revealed error patterns in difficult cases. These results position Hireca and CytoMap as a transparent framework for clinically reviewable biomarker assessment directly from routine HE histology.
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