Structured Report

PathOrchestra is a versatile pathology foundation model from Shanghai AI Lab and multiple Chinese institutions, trained via self-supervised learning on 287,424 H&E-stained WSIs from 21 tissue types across 3 independent clinical centers. Evaluated on the largest known clinical task benchmark (112 tasks: 61 private + 51 public) spanning digital slide preprocessing, pan-cancer classification (17 cancer types), lesion identification, multi-cancer subtype classification (36 tasks), biomarker assessment (36 tasks), gene expression prediction, and structured report generation. Achieves over 0.950 accuracy in 47 tasks. First model to generate structured pathology reports for colorectal cancer and lymphoma. Apache 2.0 open-source license.

The complexity and variability of high-resolution pathological images present significant challenges in computational pathology. We present PathOrchestra, a versatile pathology foundation model trained via self-supervised learning on 287,424 slides from 21 tissue types across three centers. Evaluated on 112 tasks from 61 private and 51 public datasets, covering digital slide preprocessing, pan-cancer classification, lesion identification, multi-cancer subtype classification, biomarker assessment, gene expression prediction, and structured report generation. Across 27,755 WSIs and 9,415,729 ROI images, it achieved over 0.950 accuracy in 47 tasks. It is the first to generate structured reports for colorectal cancer and lymphoma.