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Two open academic pathology models: UNI reads tissue images, CONCH links images with pathology text.
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.
UNI is trained with DINOv2 self-supervision; CONCH uses contrastive image-text alignment.
One technology page on OnCo cites this paper by its DOI; this record gives the citation a page of its own so a reader can follow it without leaving OnCo. Read the abstract above alongside the citing page listed under Related; the record was created automatically from the Europe PMC entry and its figures have not been checked by hand.
Two technology pages and one idea page on OnCo cite this paper by its DOI; this record gives the citation a page of its own so a reader can follow it without leaving OnCo. Read the abstract above alongside the citing pages listed under Related; the record was created automatically from the Europe PMC entry and its figures have not been checked by hand.
Query for this technology: (TITLE:"UNI and CONCH" OR ABSTRACT:"UNI and CONCH" OR TITLE:"Harvard, Mahmood Lab" OR ABSTRACT:"Harvard, Mahmood Lab") AND (cancer OR tumor OR tumour OR oncology OR carcinoma OR lymphoma OR leukemia OR leukaemia OR myeloma OR sarcoma OR melanoma OR glioma). Results are unfiltered search hits about UNI and CONCH (Harvard, Mahmood Lab), not a curated reading list.
Shares Pathology & radiology foundation models, AI in oncology roadmap: pattern readers → foundation models → agents in the workflow, Dana-Farber Brigham Cancer Center and the tags foundation-model, pathology.
Shares Pathology & radiology foundation models, AI in oncology roadmap: pattern readers → foundation models → agents in the workflow and the tags foundation-model, pathology.
Shares Pathology & radiology foundation models, AI in oncology roadmap: pattern readers → foundation models → agents in the workflow and the tags foundation-model, pathology.
Shares Pathology & radiology foundation models, AI in oncology roadmap: pattern readers → foundation models → agents in the workflow and the tags foundation-model, pathology.
Shares Pathology & radiology foundation models, AI in oncology roadmap: pattern readers → foundation models → agents in the workflow and the tags foundation-model, pathology.
Shares Pathology & radiology foundation models and the tags foundation-model, pathology.
Shares Pathology & radiology foundation models and the tags foundation-model, pathology.
Shares Pathology & radiology foundation models and the tags foundation-model, pathology.
Open-source projects that implement or serve this technology, from OnCo's own catalogue: licence and last activity as the repository reported them on the day of the fetch. Listing is not endorsement; check the licence before reuse and the validation before clinical use.
A general-purpose pathology foundation model trained on 100 million tissue patches; weights are gated on Hugging Face under a non-commercial licence.
A vision-language foundation model for pathology paired with UNI; gated weights, non-commercial.