# UNI and CONCH (Harvard, Mahmood Lab)

Source: https://onco.cc/technologies/uni-conch/  
OnCo record `uni-conch` (Technology). Data CC BY-NC 4.0, attribute "Data from OnCo (onco.cc)"; commercial use needs a licence.

## TL;DR

Two open academic pathology models: UNI reads tissue images, CONCH links images with pathology text.

## Summary

UNI and CONCH are two open academic pathology foundation models from the Mahmood Lab at Harvard. UNI is a vision encoder trained with DINOv2 self-supervision on 100 million tiles from 100,000 slides (Nature Medicine 2024), with the larger UNI2-h scaling it further. CONCH (Nature Medicine 2024) is a vision-language model trained on 1.17 million image-caption pairs by contrastive alignment, which enables zero-shot classification and image or text retrieval without task-specific training. Both are released with open weights under a non-commercial licence and are widely used as reproducible baselines for cancer subtyping, biomarker prediction and prognosis. They work at tile level, so slide-level decisions need an aggregation step, and clinical validation is task by task. For a newcomer, UNI learns what tissue looks like and CONCH learns what pathologists say about it.

## Fields

- Kind: Technology
- Status: emerging
- Last checked: 2026-09-08
- Tags: foundation-model; pathology
- Principle: UNI is trained with DINOv2 self-supervision; CONCH uses contrastive image-text alignment.
- Since: 2024
- Strengths: Open weights for research; Reproducible baselines
- Limitations: Non-commercial licence; Tile-level, needs aggregation

## Sources

- UNI, Nature Medicine 2024: https://doi.org/10.1038/s41591-024-02857-3
- CONCH, Nature Medicine 2024: https://doi.org/10.1038/s41591-024-02856-4

## Connected records

- fronts: [AI & Computation](https://onco.cc/fronts/ai-computation/)
- technologies: [Pathology & radiology foundation models](https://onco.cc/technologies/pathology-foundation-model/), [TITAN (whole-slide multimodal model)](https://onco.cc/technologies/titan/)
- institutions: [Dana-Farber Brigham Cancer Center](https://onco.cc/institutions/dana-farber/)
- key papers: [A visual-language foundation model for computational pathology](https://onco.cc/key-papers/paper-lu-nat-med/), [Towards a general-purpose foundation model for computational pathology](https://onco.cc/key-papers/paper-chen-nat-med/)
- roadmaps: [AI in oncology roadmap: pattern readers → foundation models → agents in the workflow](https://onco.cc/roadmaps/ai-oncology-roadmap/)

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