Towards Cellular-Scale Interpretability in Pathology Foundation Models for Biomarker Assessment
Authors: Jingsong Liu, Han Li, Zhengyang Xu, Franz-Leonard Klaus, Fabian Stögbauer, Shihui Zu, Weiwei Zhou, Atsuko Kasajima, +14 more
Organizations: Institute of Pathology, Technical University of Munich, Munich, Germany · Munich Center for Machine Learning (MCML), Munich, Germany · Munich Data Science Institute (MDSI), Munich, Germany · School of Computation, Information and Technology, Technical University of Munich, Munich, Germany · Computer Aided Medical Procedures (CAMP), Technical University of Munich, Munich, Germany · School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, China · Affiliated Hospital of Chifeng University, Chifeng, China · The First Hospital and the College of Basic Medical Sciences of China Medical University, Shenyang, China · Center for Medical Imaging, Robotics, and Analytic Computing & Learning (MIRACLE), Suzhou Institute for Advanced Research, USTC, Suzhou, China
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.