PhenoLIP: Integrating Phenotype Ontology Knowledge into Medical Vision-Language Pretraining
This work addresses the need for more structured and interpretable medical image understanding in medical AI, though it is incremental as it builds on existing CLIP-like models with a novel knowledge integration approach.
The paper tackles the problem of medical vision-language models failing to capture systematic visual knowledge from phenotype ontologies by constructing PhenoKG, a large-scale phenotype-centric multimodal knowledge graph, and proposing PhenoLIP, a pretraining framework that integrates structured phenotype knowledge. The result shows PhenoLIP outperforms previous state-of-the-art baselines, improving phenotype classification accuracy by 8.85% and cross-modal retrieval by 15.03%.
Recent progress in large-scale CLIP-like vision-language models(VLMs) has greatly advanced medical image analysis. However, most existing medical VLMs still rely on coarse image-text contrastive objectives and fail to capture the systematic visual knowledge encoded in well-defined medical phenotype ontologies. To address this gap, we construct PhenoKG, the first large-scale, phenotype-centric multimodal knowledge graph that encompasses over 520K high-quality image-text pairs linked to more than 3,000 phenotypes. Building upon PhenoKG, we propose PhenoLIP, a novel pretraining framework that explicitly incorporates structured phenotype knowledge into medical VLMs through a two-stage process. We first learn a knowledge-enhanced phenotype embedding space from textual ontology data and then distill this structured knowledge into multimodal pretraining via a teacher-guided knowledge distillation objective. To support evaluation, we further introduce PhenoBench, an expert-verified benchmark designed for phenotype recognition, comprising over 7,800 image--caption pairs covering more than 1,000 phenotypes. Extensive experiments demonstrate that PhenoLIP outperforms previous state-of-the-art baselines, improving upon BiomedCLIP in phenotype classification accuracy by 8.85\% and BIOMEDICA in cross-modal retrieval by 15.03%, underscoring the value of integrating phenotype-centric priors into medical VLMs for structured and interpretable medical image understanding.