KEEP

モデル紹介

**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]

主な特徴

特点摘要待依据来源补齐，不凭模型名称推断能力。

制約と注意点

具体的な制約を判断する出典が不足しています。実際の性能、遅延、データ方針、ライセンス、提供期間を確認してください。

出典別料金と費用分析

料金はチャネル・地域・設定によって異なります。各チャネルの料金は個別に表示し、提供元の統一公式料金とは見なしません。

公開評価と実行設定

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

よくある質問

出典と追跡記録

许可证：mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。