# Pathology foundation models: UNI, UNI2, Virchow2, CTransPath, CONCH, TITAN

Source: https://onco.cc/terms/pathology-foundation-models/  
OnCo record `pathology-foundation-models` (Term). Data CC BY-NC 4.0, attribute "Data from OnCo (onco.cc)"; commercial use needs a licence.

## TL;DR

Pathology foundation models are image encoders pretrained without labels on millions of slide tiles; UNI and CONCH come from the Mahmood Lab at Harvard, Virchow from Paige, and CTransPath was an early transformer version.

## Summary

UNI, from Chen and colleagues, is a general-purpose self-supervised pathology encoder trained on over a hundred million tiles and evaluated across dozens of tasks; UNI2-h is its larger successor released on Hugging Face under a gated non-commercial licence. Virchow, from Vorontsov and colleagues at Paige, was trained on about 1.5 million slides for clinical-grade and rare cancer detection, with Virchow2 released the same way. CTransPath (Wang and colleagues, 2022) showed contrastive pretraining of a transformer for histology, and CONCH aligns tiles with pathology text; TITAN extends the family to whole slides and reports. Gated weights are open for research but not for products, which is why OnCo records their openness as gated.

## Fields

- Kind: Term
- Last checked: 2026-09-24
- Also known as: UNI2; UNI2-h; Virchow2; CTransPath; TransPath; pathology encoder; tile encoder; histopathology foundation model
- Tags: cansim-terms

## Notes

- Listed in the CanSim terms map 1.0.0 (docs/onco/terms.json, generated 2026-09-24), CC BY 4.0, attribution: CanSim project, an open, public-data-first cancer foundation-model programme; CanSim page path /terms/mahmood-lab.

## Sources

- Chen et al., UNI: towards a general-purpose foundation model for computational pathology (Nature Medicine 2024): https://doi.org/10.1038/s41591-024-02857-3
- UNI2-h on Hugging Face (MahmoodLab): https://huggingface.co/MahmoodLab/UNI2-h
- Vorontsov et al., Virchow: a foundation model for clinical-grade computational pathology (Nature Medicine 2024): https://doi.org/10.1038/s41591-024-03141-0
- Virchow2 on Hugging Face (Paige): https://huggingface.co/paige-ai/Virchow2
- Wang et al., CTransPath: transformer-based unsupervised contrastive learning for histopathological image classification (Medical Image Analysis 2022): https://doi.org/10.1016/j.media.2022.102559
- Lu et al., CONCH: a visual-language foundation model for computational pathology (Nature Medicine 2024): https://doi.org/10.1038/s41591-024-02856-4

## Connected records

- companies: [Paige AI](https://onco.cc/companies/paige/)
- terms: [Attention-based multiple-instance learning (ABMIL, CLAM)](https://onco.cc/terms/abmil/), [Cancer AI vocabulary (CanSim terms map)](https://onco.cc/terms/cancer-ai-vocabulary/), [Foundation model](https://onco.cc/terms/foundation-model/), [Open weights, open code and gated models](https://onco.cc/terms/open-weights/), [Tile and patch encoding of slides](https://onco.cc/terms/tile-patch-encoding/)
- technologies: [Pathology & radiology foundation models](https://onco.cc/technologies/pathology-foundation-model/), [TITAN (whole-slide multimodal model)](https://onco.cc/technologies/titan/)

---
JSON: https://onco.cc/api/v1/entities/pathology-foundation-models.json