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KEEP

Astaxanthin

**KEEP** (**K**nowledg**E**-**E**nhanced **P**athology) is a foundation model designed for cancer diagnosis that integrates disease knowledge into vision-language pre-training. It utilizes a comprehensive disease knowledge graph (KG) containing 11,454 human diseases and 139,143 disease attributes, such as synonyms, definitions, and hierarchical relationships. KEEP reorganizes millions of publicly available noisy pathology image-text pairs into 143K well-structured semantic groups based on the hierarchical relations of the disease KG. By incorporating disease knowledge into the alignment process, KEEP achieves more nuanced image and text representations. The model is validated on 18 diverse benchmarks with over 14,000 whole-slide images (WSIs), demonstrating state-of-the-art performance in zero-shot cancer diagnosis, including an average sensitivity of 89.8% for cancer detection across 7 cancer types. KEEP also excels in subtyping rare cancers, achieving strong generalizability in diagnosing rare tumor subtypes. - **Developed by:** MAGIC-AI4Med team from Shanghai Jiao Tong University and Shanghai AI Lab. - **Model type:** Vision-language models (vision encoder: ViT-L/16; text encoder: Bert) - **Pretrain datasets:** 143K pathology semantic groups, each with a single caption and multiple images. - **License:** MIT - **Repository:** https://github.com/MAGIC-AI4Med/KEEP - **Paper [optional]:** https://www.cell.com/cancer-cell/fulltext/S1535-6108(26)00058-9 - **Demo [optional]:** [More Information Needed]

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KEEP

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