EXGRA-MED: Extended Context Graph Alignment for Medical Vision- Language Models

• URL: http://arxiv.org/abs/2410.02615v3
• Date: Tue, 17 Jun 2025 22:50:07 GMT
• Title: EXGRA-MED: Extended Context Graph Alignment for Medical Vision- Language Models
• Authors: Duy M. H. Nguyen, Nghiem T. Diep, Trung Q. Nguyen, Hoang-Bao Le, Tai Nguyen, Tien Nguyen, TrungTin Nguyen, Nhat Ho, Pengtao Xie, Roger Wattenhofer, James Zou, Daniel Sonntag, Mathias Niepert,
• Abstract summary: We introduce EXGRA-MED, a novel framework for vision-language integration in medical AI.<n>It jointly aligns images, instruction responses, and extended captions in the latent space, advancing semantic grounding and cross-modal coherence.<n>It matches LLAVA-MED's performance using just 10% of pre-training data, achieving a 20.13% gain on VQA-RAD and approaching full-data performance.
• Score: 69.40730368630003
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: State-of-the-art medical multi-modal LLMs (med-MLLMs), such as LLAVA-MED and BIOMEDGPT, primarily depend on scaling model size and data volume, with training driven largely by autoregressive objectives. However, we reveal that this approach can lead to weak vision-language alignment, making these models overly dependent on costly instruction-following data. To address this, we introduce EXGRA-MED, a novel multi-graph alignment framework that jointly aligns images, instruction responses, and extended captions in the latent space, advancing semantic grounding and cross-modal coherence. To scale to large LLMs (e.g., LLaMa-7B), we develop an efficient end-to-end training scheme using black-box gradient estimation, enabling fast and scalable optimization. Empirically, EXGRA-MED matches LLAVA-MED's performance using just 10% of pre-training data, achieving a 20.13% gain on VQA-RAD and approaching full-data performance. It also outperforms strong baselines like BIOMEDGPT and RADFM on visual chatbot and zero-shot classification tasks, demonstrating its promise for efficient, high-quality vision-language integration in medical AI.

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