PhenoLIP: Integrating Phenotype Ontology Knowledge into Medical Vision-Language Pretraining

• URL: http://arxiv.org/abs/2602.06184v1
• Date: Thu, 05 Feb 2026 20:44:07 GMT
• Title: PhenoLIP: Integrating Phenotype Ontology Knowledge into Medical Vision-Language Pretraining
• Authors: Cheng Liang, Chaoyi Wu, Weike Zhao, Ya Zhang, Yanfeng Wang, Weidi Xie,
• Abstract summary: PhenoLIP is a novel pretraining framework that incorporates structured phenotype knowledge into medical image understanding.<n> PhenoLIP outperforms previous state-of-the-art approaches for medical image understanding.
• Score: 71.60950593762719
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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