PRIMA framework enhances medical diagnosis by integrating image and clinical data

Researchers have developed PRIMA, a novel framework designed to enhance medical diagnosis by integrating visual information with clinical metadata. PRIMA refines the Clinical ModernBERT model using a curated corpus of risk-disease correlations, improving its ability to understand clinical descriptions. The framework employs a dual-encoder pre-training strategy with DINOv3 and the enhanced Clinical ModernBERT, optimized through four complementary loss functions to align multi-granular semantic information and handle ambiguity. Finally, Qwen3 is utilized to fuse these aligned features for precise disease classification, demonstrating superior performance over existing methods without excessive computational demands. AI

IMPACT This framework could improve the accuracy and efficiency of medical diagnoses by better leveraging diverse data sources.

RANK_REASON The cluster describes a new research paper detailing a novel framework for medical diagnosis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• arXiv
• Clinical ModernBERT
• DINOv3
• Hugging Face
• Qwen3
• retrieval-augmented generation
• Yiqing Wang

• paper
• model release
• product

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