Self-Supervised

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.

UNI is a general-purpose self-supervised foundation model for computational pathology from Mahmood Lab (Harvard/BWH), pretrained on >100 million images from >100,000 H&E-stained WSIs (>77 TB) across 20 tissue types. Evaluated on 34 representative CPath tasks — outperforming prior models across cancer classification, organ transplant assessment, and rare disease analysis. Demonstrates resolution-agnostic classification, few-shot slide classification, and generalization to 108 cancer types in the OncoTree system. 1,300+ citations.

Quantitative evaluation of tissue images is crucial for computational pathology tasks. The high resolution of WSIs and the variability of morphological features present significant challenges. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using more than 100 million images from over 100,000 diagnostic H&E-stained WSIs across 20 major tissue types. The model was evaluated on 34 representative CPath tasks. UNI outperforms previous state-of-the-art models and demonstrates new capabilities including resolution-agnostic tissue classification, few-shot slide classification, and disease subtyping generalization to 108 cancer types.

Virchow is the first million-slide foundation model for computational pathology, developed by Paige in collaboration with Microsoft. A 632M-parameter ViT-H model trained using DINOv2 on 1.5 million H&E-stained WSIs from MSKCC (17 tissue types). Demonstrates clinical-grade pan-cancer detection with 0.95 AUC across nine common and seven rare cancers. With less training data, the pan-cancer detector built on Virchow achieves similar performance to tissue-specific clinical-grade models in production, outperforming them on rare cancer variants. Serves as the foundation for Paige's Virchow2 (3M WSIs, multimodal) and Virchow2G (1.8B parameters) models.

The analysis of histopathology images with artificial intelligence aims to enable clinical decision support systems and precision medicine. We present Virchow, the largest foundation model for computational pathology to date. In addition to the evaluation of biomarker prediction and cell identification, we demonstrate that a large foundation model enables pan-cancer detection, achieving 0.95 specimen-level AUC across nine common and seven rare cancers. With less training data, the pan-cancer detector built on Virchow achieved similar performance to tissue-specific clinical-grade models in production and outperformed them on some rare variants of cancer.