{"count":13,"items":[{"article":null,"cancer_slugs":["pan-cancer"],"category":"clinical-LLM","confidence":"high","developer":"Microsoft Research","hf":{"downloads":220044,"fetched_at":"2026-09-09T21:33:09Z","gated":false,"last_modified":"2023-11-06","library":"transformers","license":"mit","likes":335,"pipeline_tag":"fill-mask"},"kind":"foundation","license":"MIT","links":{"demo":null,"docs":null,"doi":"10.1145/3458754","github":null,"huggingface":"https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext","paper":"https://dl.acm.org/doi/10.1145/3458754","pmid":null},"modalities":["literature","clinical-text"],"name":"BiomedBERT (PubMedBERT)","openness":"open-weights","regulatory_status":"not-applicable","release_date":"2020-07-31","settings":["basic-research","clinical-trials"],"slug":"biomedbert","summary":"BERT pretrained from scratch on PubMed abstracts and PMC full text with a biomedical vocabulary; the workhorse encoder for named-entity recognition, relation extraction and classification over oncology literature and reports.","tasks":["information-extraction","classification","feature-extraction"],"url":"/ai-oncology/models/biomedbert","verified_at":"2026-09-05T22:26:00.196585","version":null},{"article":null,"cancer_slugs":["pan-cancer"],"category":"multimodal","confidence":"high","developer":"Microsoft Research","hf":{"downloads":249031,"fetched_at":"2026-09-09T21:33:08Z","gated":false,"last_modified":"2025-01-14","library":"open_clip","license":"mit","likes":424,"pipeline_tag":"zero-shot-image-classification"},"kind":"foundation","license":"MIT","links":{"demo":null,"docs":null,"doi":null,"github":"https://github.com/microsoft/BiomedCLIP_data_pipeline","huggingface":"https://huggingface.co/microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224","paper":"https://ai.nejm.org/doi/full/10.1056/AIoa2400640","pmid":null},"modalities":["multimodal","histopathology","radiology-xray","radiology-ct","literature"],"name":"BiomedCLIP","openness":"open-weights","regulatory_status":"research-only","release_date":"2023-03-02","settings":["basic-research","education"],"slug":"biomedclip","summary":"CLIP-style vision-language model pretrained on PMC-15M \u2014 15 million figure\u2013caption pairs from biomedical papers \u2014 with a PubMedBERT text tower and a ViT-B image tower; supports zero-shot classification and retrieval across pathology, radiology and more.","tasks":["image-text-retrieval","classification","feature-extraction","question-answering"],"url":"/ai-oncology/models/biomedclip","verified_at":"2026-09-05T22:26:00.027245","version":null},{"article":null,"cancer_slugs":["pan-cancer"],"category":"pathology","confidence":"medium","developer":"Yu Lab, Harvard Medical School","hf":null,"kind":"foundation","license":"non-commercial (see repository)","links":{"demo":null,"docs":null,"doi":"10.1038/s41586-024-07894-z","github":"https://github.com/hms-dbmi/CHIEF","huggingface":null,"paper":"https://www.nature.com/articles/s41586-024-07894-z","pmid":null},"modalities":["histopathology"],"name":"CHIEF","openness":"open-weights","regulatory_status":"research-only","release_date":"2024-09-04","settings":["diagnosis","prognosis","basic-research"],"slug":"chief","summary":"Clinical Histopathology Imaging Evaluation Foundation model: trained on 60,530 whole-slide images across 19 anatomical sites and validated on 19,491 slides from 24 hospitals for cancer detection, tumour-origin prediction, genomic profiling and survival.","tasks":["feature-extraction","classification","prognosis","survival-analysis"],"url":"/ai-oncology/models/chief","verified_at":"2026-09-05T22:26:01.490013","version":null},{"article":null,"cancer_slugs":["pan-cancer"],"category":"pathology","confidence":"high","developer":"Mahmood Lab, Brigham and Women's Hospital / Harvard Medical School","hf":{"downloads":58735,"fetched_at":"2026-09-09T21:33:09Z","gated":"auto","last_modified":"2024-05-05","library":"timm","license":"cc-by-nc-nd-4.0","likes":204,"pipeline_tag":"image-feature-extraction"},"kind":"foundation","license":"cc-by-nc-nd-4.0","links":{"demo":null,"docs":null,"doi":"10.1038/s41591-024-02856-4","github":"https://github.com/mahmoodlab/CONCH","huggingface":"https://huggingface.co/MahmoodLab/CONCH","paper":"https://www.nature.com/articles/s41591-024-02856-4","pmid":null},"modalities":["histopathology","clinical-text","multimodal"],"name":"CONCH","openness":"gated-weights","regulatory_status":"research-only","release_date":"2024-03-19","settings":["basic-research","diagnosis","education"],"slug":"conch","summary":"Vision-language foundation model for pathology: an image encoder and a text encoder trained together on 1.17 million histopathology image\u2013caption pairs, enabling zero-shot classification and image\u2013text retrieval without labelled slides.","tasks":["feature-extraction","classification","image-text-retrieval","segmentation"],"url":"/ai-oncology/models/conch","verified_at":"2026-09-05T22:26:01.107992","version":"CONCH (v1)"},{"article":null,"cancer_slugs":["pan-cancer"],"category":"genomics","confidence":"high","developer":"Google","hf":null,"kind":"tool","license":"BSD-3-Clause","links":{"demo":null,"docs":null,"doi":"10.1038/nbt.4235","github":"https://github.com/google/deepvariant","huggingface":null,"paper":"https://www.nature.com/articles/nbt.4235","pmid":null},"modalities":["genomics"],"name":"DeepVariant","openness":"open-weights","regulatory_status":"research-only","release_date":"2017-12-04","settings":["diagnosis","basic-research"],"slug":"deepvariant","summary":"Deep-learning variant caller that turns aligned sequencing reads into pileup images and classifies genotypes with a CNN; widely used for germline calling, with the companion DeepSomatic extending the approach to tumour\u2013normal somatic variants.","tasks":["variant-effect","classification"],"url":"/ai-oncology/models/deepvariant","verified_at":"2026-09-05T22:26:01.073225","version":null},{"article":null,"cancer_slugs":["pan-cancer"],"category":"pathology","confidence":"medium","developer":"Bioptimus","hf":{"downloads":80579,"fetched_at":"2026-09-09T21:33:09Z","gated":"auto","last_modified":"2025-12-12","library":"timm","license":"apache-2.0","likes":92,"pipeline_tag":"image-feature-extraction"},"kind":"foundation","license":"apache-2.0","links":{"demo":null,"docs":null,"doi":null,"github":null,"huggingface":"https://huggingface.co/bioptimus/H-optimus-0","paper":null,"pmid":null},"modalities":["histopathology"],"name":"H-optimus-0","openness":"gated-weights","regulatory_status":"research-only","release_date":"2024-07-10","settings":["basic-research","diagnosis","prognosis"],"slug":"h-optimus-0","summary":"1.1-billion-parameter ViT-g/14 pathology encoder trained on more than 500,000 H&E slides (hundreds of millions of tiles), released under Apache-2.0 \u2014 one of the few large pathology foundation models with a permissive licence.","tasks":["feature-extraction","classification","prognosis"],"url":"/ai-oncology/models/h-optimus-0","verified_at":"2026-09-07T17:57:59.478713","version":null},{"article":null,"cancer_slugs":["pan-cancer"],"category":"clinical-LLM","confidence":"medium","developer":"Google (Health AI Developer Foundations)","hf":{"downloads":1105085,"fetched_at":"2026-09-09T21:33:09Z","gated":"auto","last_modified":"2025-10-28","library":"transformers","license":"other","likes":1048,"pipeline_tag":"image-text-to-text"},"kind":"foundation","license":"Health AI Developer Foundations terms of use","links":{"demo":null,"docs":"https://developers.google.com/health-ai-developer-foundations/medgemma","doi":null,"github":null,"huggingface":"https://huggingface.co/google/medgemma-4b-it","paper":"https://arxiv.org/abs/2507.05201","pmid":null},"modalities":["multimodal","radiology-xray","histopathology","dermoscopy","clinical-text"],"name":"MedGemma","openness":"gated-weights","regulatory_status":"research-only","release_date":"2025-05-20","settings":["basic-research","education"],"slug":"medgemma","summary":"Open-weight medical vision-language models built on Gemma 3: the 4B variant reads chest X-rays, dermatology, ophthalmology and histopathology images alongside text; the 27B variant targets medical text reasoning. Meant as a starting point for developers to fine-tune, not as a finished clinical product.","tasks":["question-answering","report-generation","classification","information-extraction"],"url":"/ai-oncology/models/medgemma","verified_at":"2026-09-05T22:26:00.309905","version":"4B multimodal / 27B text"},{"article":null,"cancer_slugs":["pan-cancer"],"category":"pathology","confidence":"medium","developer":"Owkin","hf":{"downloads":315410,"fetched_at":"2026-09-09T21:33:09Z","gated":false,"last_modified":"2026-01-19","library":"transformers","license":"other","likes":43,"pipeline_tag":"image-feature-extraction"},"kind":"foundation","license":"owkin non-commercial","links":{"demo":null,"docs":null,"doi":null,"github":null,"huggingface":"https://huggingface.co/owkin/phikon-v2","paper":"https://arxiv.org/abs/2409.09173","pmid":null},"modalities":["histopathology"],"name":"Phikon-v2","openness":"open-weights","regulatory_status":"research-only","release_date":"2024-09-13","settings":["basic-research","prognosis"],"slug":"phikon-v2","summary":"ViT-L pathology encoder trained with DINOv2 on PANCAN-XL \u2014 456 million tiles from 58,359 whole-slide images that mix public cohorts (TCGA, CPTAC, GTEx and others) with private data \u2014 positioned for biomarker prediction.","tasks":["feature-extraction","classification","prognosis"],"url":"/ai-oncology/models/phikon-v2","verified_at":"2026-09-05T22:26:01.463515","version":null},{"article":null,"cancer_slugs":["pan-cancer"],"category":"pathology","confidence":"high","developer":"Microsoft Research / Providence Health / University of Washington","hf":{"downloads":45031,"fetched_at":"2026-09-09T21:33:08Z","gated":"auto","last_modified":"2026-08-07","library":"timm","license":"apache-2.0","likes":189,"pipeline_tag":"image-feature-extraction"},"kind":"foundation","license":"apache-2.0","links":{"demo":null,"docs":null,"doi":"10.1038/s41586-024-07441-w","github":"https://github.com/prov-gigapath/prov-gigapath","huggingface":"https://huggingface.co/prov-gigapath/prov-gigapath","paper":"https://www.nature.com/articles/s41586-024-07441-w","pmid":null},"modalities":["histopathology","multimodal"],"name":"Prov-GigaPath","openness":"gated-weights","regulatory_status":"research-only","release_date":"2024-05-22","settings":["basic-research","diagnosis","prognosis"],"slug":"prov-gigapath","summary":"Whole-slide foundation model with 1.3 billion parameters, pretrained on 1.3 billion tiles from 171,189 slides of real-world clinical data; pairs a DINOv2 tile encoder with a LongNet slide encoder that reasons over an entire slide.","tasks":["feature-extraction","classification","prognosis","image-text-retrieval"],"url":"/ai-oncology/models/prov-gigapath","verified_at":"2026-09-05T22:26:01.122323","version":null},{"article":null,"cancer_slugs":["pan-cancer"],"category":"pathology","confidence":"medium","developer":"Paige / Memorial Sloan Kettering Cancer Center","hf":{"downloads":110111,"fetched_at":"2026-09-09T21:33:08Z","gated":"auto","last_modified":"2024-10-26","library":"timm","license":"cc-by-nc-nd-4.0","likes":151,"pipeline_tag":"image-feature-extraction"},"kind":"foundation","license":"cc-by-nc-nd-4.0","links":{"demo":null,"docs":null,"doi":null,"github":null,"huggingface":"https://huggingface.co/paige-ai/Virchow2","paper":"https://arxiv.org/abs/2408.00738","pmid":null},"modalities":["histopathology"],"name":"Virchow2","openness":"gated-weights","regulatory_status":"research-only","release_date":"2024-08-01","settings":["basic-research","diagnosis"],"slug":"virchow2","summary":"Successor to Virchow: ViT-H/14 pretrained on 3.1 million whole-slide images from about 225,000 patients across 45 countries, at mixed magnifications (5\u00d7\u201340\u00d7), with pathology-specific augmentations.","tasks":["feature-extraction","classification","detection"],"url":"/ai-oncology/models/virchow2","verified_at":"2026-09-05T22:26:01.142049","version":null},{"article":null,"cancer_slugs":["pan-cancer"],"category":"pathology","confidence":"high","developer":"Mahmood Lab, Brigham and Women's Hospital / Harvard Medical School","hf":{"downloads":40388,"fetched_at":"2026-09-09T21:33:08Z","gated":"auto","last_modified":"2025-03-06","library":"timm","license":"cc-by-nc-nd-4.0","likes":372,"pipeline_tag":"image-feature-extraction"},"kind":"foundation","license":"cc-by-nc-nd-4.0","links":{"demo":null,"docs":null,"doi":"10.1038/s41591-024-02857-3","github":"https://github.com/mahmoodlab/UNI","huggingface":"https://huggingface.co/MahmoodLab/UNI","paper":"https://www.nature.com/articles/s41591-024-02857-3","pmid":null},"modalities":["histopathology"],"name":"UNI","openness":"gated-weights","regulatory_status":"research-only","release_date":"2024-03-19","settings":["basic-research","diagnosis","prognosis"],"slug":"uni","summary":"General-purpose self-supervised vision encoder for H&E histopathology tiles, pretrained on more than 100 million tiles from over 100,000 whole-slide images; the reference foundation model for pathology feature extraction.","tasks":["feature-extraction","classification","detection","prognosis"],"url":"/ai-oncology/models/uni","verified_at":"2026-09-05T22:26:01.084174","version":"UNI (v1)"},{"article":null,"cancer_slugs":["pan-cancer"],"category":"pathology","confidence":"high","developer":"Paige / Memorial Sloan Kettering Cancer Center","hf":{"downloads":7616,"fetched_at":"2026-09-09T21:33:08Z","gated":"auto","last_modified":"2024-09-03","library":"timm","license":"apache-2.0","likes":76,"pipeline_tag":"image-feature-extraction"},"kind":"foundation","license":"cc-by-nc-nd-4.0","links":{"demo":null,"docs":null,"doi":"10.1038/s41591-024-03141-0","github":null,"huggingface":"https://huggingface.co/paige-ai/Virchow","paper":"https://www.nature.com/articles/s41591-024-03141-0","pmid":null},"modalities":["histopathology"],"name":"Virchow","openness":"gated-weights","regulatory_status":"research-only","release_date":"2024-07-22","settings":["diagnosis","basic-research"],"slug":"virchow","summary":"632-million-parameter vision transformer pretrained on 1.5 million whole-slide images from about 100,000 patients \u2014 the largest pathology pretraining set at its release \u2014 and used to build a pan-cancer detection model covering 17 cancer types, including rare ones.","tasks":["feature-extraction","classification","detection"],"url":"/ai-oncology/models/virchow","verified_at":"2026-09-05T22:26:01.133062","version":"Virchow (v1)"},{"article":null,"cancer_slugs":["prostate"],"category":"pathology","confidence":"high","developer":"Paige","hf":null,"kind":"product","license":"proprietary","links":{"demo":null,"docs":"https://www.accessdata.fda.gov/cdrh_docs/pdf20/DEN200080.pdf","doi":null,"github":null,"huggingface":null,"paper":null,"pmid":null},"modalities":["histopathology"],"name":"Paige Prostate Detect","openness":"closed","regulatory_status":"fda-de-novo","release_date":"2021-09-21","settings":["diagnosis"],"slug":"paige-prostate","summary":"The first AI software for pathology authorised by the US FDA (De Novo, September 2021): flags prostate biopsy whole-slide images likely to contain cancer so the pathologist reviews the suspicious region.","tasks":["detection","classification"],"url":"/ai-oncology/models/paige-prostate","verified_at":"2026-09-07T07:06:50.537219","version":null}],"standard":"https://cancer3.ai/docs/ai-model-card","vocab":{"confidence":["high","medium","low"],"dataset_access":["open","registration","dua","controlled","commercial"],"dataset_kinds":["dataset","registry","benchmark","biobank","corpus"],"formats":["SVS","TIFF","NDPI","DICOM","NIfTI","PNG","JPEG","CSV","TSV","JSON","Parquet","FASTQ","BAM","VCF","MAF","h5ad","PDB","mmCIF","text","other"],"kinds":["foundation","task-model","tool","framework","product"],"link_roles":["pretraining","training","finetuning","evaluation","benchmark"],"modalities":["histopathology","radiology-ct","radiology-mri","radiology-xray","mammography","pet","ultrasound","dermoscopy","endoscopy","genomics","transcriptomics","single-cell","proteomics","protein-sequence","molecules","clinical-text","literature","ehr","multimodal","immunopeptidomics"],"need_status":["open","in-progress","solved"],"openness":["open-weights","gated-weights","api-only","closed"],"regulatory":["research-only","fda-cleared","fda-de-novo","fda-pma","ce-marked","ce-ivdr","not-applicable"],"settings":["basic-research","screening","diagnosis","prognosis","treatment-planning","treatment-response","drug-discovery","clinical-trials","education","vaccine-design","immunotherapy"],"tasks":["feature-extraction","classification","segmentation","detection","screening","risk-prediction","prognosis","treatment-response","survival-analysis","image-text-retrieval","report-generation","question-answering","information-extraction","clinical-trial-matching","protein-structure","variant-effect","gene-expression","single-cell","drug-discovery","drug-repurposing","peptide-mhc-binding","antigen-presentation","neoantigen-prioritisation"]}}