Cancer Cell

KEEP (KnowledgE-Enhanced Pathology) is a vision-language foundation model from Shanghai AI Lab / SJTU that systematically integrates disease knowledge into pretraining for cancer diagnosis. Uses a comprehensive disease knowledge graph with 11,454 diseases and 139,143 attributes from DO and UMLS to reorganize millions of pathology image-text pairs into 143,000 semantically structured groups aligned with disease ontology hierarchies. Across 18 public benchmarks (14,000+ WSIs) and 4 institutional rare cancer datasets (926 cases), KEEP consistently outperforms existing foundation models (CHIEF, CONCH, UNI), with substantial gains for rare subtypes (+8.5 pts balanced accuracy vs CONCH on 30 rare brain cancers). Published in Cancer Cell, Feb 2026.

Vision-language foundation models have shown great promise in computational pathology but remain primarily data-driven, lacking explicit integration of medical knowledge. We introduce KEEP, a foundation model that systematically incorporates disease knowledge into pretraining for cancer diagnosis. KEEP leverages a comprehensive disease knowledge graph encompassing 11,454 diseases and 139,143 attributes to reorganize millions of pathology image-text pairs into 143,000 semantically structured groups aligned with disease ontology hierarchies. Across 18 public benchmarks (over 14,000 WSIs) and 4 institutional rare cancer datasets (926 cases), KEEP consistently outperformed existing foundation models, showing substantial gains for rare subtypes.