Computational Pathology

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.