SurgVidLM: Towards Multi-grained Surgical Video Understanding with Large Language Model

• URL: http://arxiv.org/abs/2506.17873v1
• Date: Sun, 22 Jun 2025 02:16:18 GMT
• Title: SurgVidLM: Towards Multi-grained Surgical Video Understanding with Large Language Model
• Authors: Guankun Wang, Wenjin Mo, Junyi Wang, Long Bai, Kun Yuan, Ming Hu, Jinlin Wu, Junjun He, Yiming Huang, Nicolas Padoy, Zhen Lei, Hongbin Liu, Nassir Navab, Hongliang Ren,
• Abstract summary: SurgVidLM is the first video language model designed to address both full and fine-grained surgical video comprehension.<n>We introduce the StageFocus mechanism which is a two-stage framework performing the multi-grained, progressive understanding of surgical videos.<n> Experimental results demonstrate that SurgVidLM significantly outperforms state-of-the-art Vid-LLMs in both full and fine-grained video understanding tasks.
• Score: 55.13206879750197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in Multimodal Large Language Models have demonstrated great potential in the medical domain, facilitating users to understand surgical scenes and procedures. Beyond image-based methods, the exploration of Video Large Language Models (Vid-LLMs) has emerged as a promising avenue for capturing the complex sequences of information involved in surgery. However, there is still a lack of Vid-LLMs specialized for fine-grained surgical video understanding tasks, which is crucial for analyzing specific processes or details within a surgical procedure. To bridge this gap, we propose SurgVidLM, the first video language model designed to address both full and fine-grained surgical video comprehension. To train our SurgVidLM, we construct the SVU-31K dataset which consists of over 31K video-instruction pairs, enabling both holistic understanding and detailed analysis of surgical procedures. Furthermore, we introduce the StageFocus mechanism which is a two-stage framework performing the multi-grained, progressive understanding of surgical videos. We also develop the Multi-frequency Fusion Attention to effectively integrate low and high-frequency visual tokens, ensuring the retention of critical information. Experimental results demonstrate that SurgVidLM significantly outperforms state-of-the-art Vid-LLMs in both full and fine-grained video understanding tasks, showcasing its superior capability in capturing complex procedural contexts.

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