SurgVisAgent：面向多功能手术视觉增强的多模态智能体模型
SurgVisAgent: Multimodal Agentic Model for Versatile Surgical Visual Enhancement
AI总结：
本文提出基于多模态大语言模型的手术视觉智能体SurgVisAgent，通过先验模型、少样本学习与思维链推理动态识别失真类型和严重程度，统一处理低光照、过曝、运动模糊和烟雾等多种手术图像增强任务，并在综合基准上超越传统单任务模型。
摘要：
精确的手术干预对患者安全至关重要，目前已开发出先进的增强算法来辅助外科医生进行决策。尽管取得了显著进展，但这些算法通常针对特定场景下的单一任务而设计，限制了它们在复杂现实情况中的有效性。为解决这一局限，我们提出了SurgVisAgent，一个基于多模态大语言模型（MLLMs）构建的端到端智能手术视觉智能体。SurgVisAgent能够动态识别内窥镜图像中的失真类别和严重程度，从而执行多种增强任务，如低光照增强、过曝校正、运动模糊消除和烟雾去除。具体而言，为实现卓越的手术场景理解，我们设计了一个先验模型来提供领域特定知识。此外，通过上下文少样本学习和思维链（CoT）推理，SurgVisAgent能够针对广泛的失真类型和严重程度提供定制化的图像增强，从而满足外科医生的多样化需求。进一步地，我们构建了一个模拟真实手术失真的综合基准，大量实验表明SurgVisAgent超越了传统的单任务模型，凸显了其作为手术辅助统一解决方案的潜力。
英文摘要：
Precise surgical interventions are vital to patient safety, and advanced enhancement algorithms have been developed to assist surgeons in decision-making. Despite significant progress, these algorithms are typically designed for single tasks in specific scenarios, limiting their effectiveness in complex real-world situations. To address this limitation, we propose SurgVisAgent, an end-to-end intelligent surgical vision agent built on multimodal large language models (MLLMs). SurgVisAgent dynamically identifies distortion categories and severity levels in endoscopic images, enabling it to perform a variety of enhancement tasks such as low-light enhancement, overexposure correction, motion blur elimination, and smoke removal. Specifically, to achieve superior surgical scenario understanding, we design a prior model that provides domain-specific knowledge. Additionally, through in-context few-shot learning and chain-of-thought (CoT) reasoning, SurgVisAgent delivers customized image enhancements tailored to a wide range of distortion types and severity levels, thereby addressing the diverse requirements of surgeons. Furthermore, we construct a comprehensive benchmark simulating real-world surgical distortions, on which extensive experiments demonstrate that SurgVisAgent surpasses traditional single-task models, highlighting its potential as a unified solution for surgical assistance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
