Medical Large Vision Language Models with Multi-Image Visual Ability

• URL: http://arxiv.org/abs/2505.19031v1
• Date: Sun, 25 May 2025 08:31:22 GMT
• Title: Medical Large Vision Language Models with Multi-Image Visual Ability
• Authors: Xikai Yang, Juzheng Miao, Yuchen Yuan, Jiaze Wang, Qi Dou, Jinpeng Li, Pheng-Ann Heng,
• Abstract summary: We present the Med-MIM instruction dataset, comprising 83.2K medical multi-image QA pairs.<n>We fine-tune Mantis and LLaVA-Med, resulting in two specialized medical VLMs: MIM-LLaVA-Med and Med-Mantis.<n>We also develop the Med-MIM benchmark to evaluate the medical multi-image understanding capabilities of LVLMs.
• Score: 46.889345205047675
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical large vision-language models (LVLMs) have demonstrated promising performance across various single-image question answering (QA) benchmarks, yet their capability in processing multi-image clinical scenarios remains underexplored. Unlike single image based tasks, medical tasks involving multiple images often demand sophisticated visual understanding capabilities, such as temporal reasoning and cross-modal analysis, which are poorly supported by current medical LVLMs. To bridge this critical gap, we present the Med-MIM instruction dataset, comprising 83.2K medical multi-image QA pairs that span four types of multi-image visual abilities (temporal understanding, reasoning, comparison, co-reference). Using this dataset, we fine-tune Mantis and LLaVA-Med, resulting in two specialized medical VLMs: MIM-LLaVA-Med and Med-Mantis, both optimized for multi-image analysis. Additionally, we develop the Med-MIM benchmark to comprehensively evaluate the medical multi-image understanding capabilities of LVLMs. We assess eight popular LVLMs, including our two models, on the Med-MIM benchmark. Experimental results show that both Med-Mantis and MIM-LLaVA-Med achieve superior performance on the held-in and held-out subsets of the Med-MIM benchmark, demonstrating that the Med-MIM instruction dataset effectively enhances LVLMs' multi-image understanding capabilities in the medical domain.

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