arXiv 2507.146802025-07-19cs.CVcs.AI

WSI-Agents：一种用于多模态全切片图像分析的协作式多智能体系统

WSI-Agents: A Collaborative Multi-Agent System for Multi-Modal Whole Slide Image Analysis

College of Artificial Intelligence, Shenzhen University, China（深圳大学人工智能学院）；School of Computer Science, University of Nottingham Ningbo China, China（诺丁汉宁波大学计算机学院）；College of Computer Science and Software Engineering, Shenzhen University, China（深圳大学计算机科学与软件工程学院）；Department of Electrical Engineering, City University of Hong Kong, Hong Kong（香港城市大学电气工程系）；Wuyi University, China（武夷大学）

Xinheng Lyu, Yuci Liang, Wenting Chen, Meidan Ding, Jiaqi Yang, Guolin Huang, Daokun Zhang, Xiangjian He, Linlin Shen

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AI总结：

本文提出WSI-Agents协作式多智能体系统，通过任务分配、验证机制和汇总模块提升多模态全切片图像分析的任务专用准确性与多任务通用性，并在多模态WSI基准上优于现有方法。

摘要：

全切片图像（WSIs）在数字病理学中至关重要，能够在多种病理任务中实现千兆像素级别的组织分析。尽管多模态大语言模型（MLLMs）的最新进展使得通过自然语言进行多任务WSI分析成为可能，但与任务专用模型相比，它们往往表现不佳。协作式多智能体系统已成为在医疗保健领域平衡通用性与准确性的一种有前景的解决方案，但其在病理学特定领域中的潜力仍未得到充分探索。为解决这些问题，我们提出了WSI-Agents，这是一种用于多模态WSI分析的新型协作式多智能体系统。WSI-Agents将专用功能智能体与稳健的任务分配和验证机制相结合，通过三个组件来提升任务专用准确性和多任务通用性：（1）任务分配模块，利用包含补丁级和WSI级MLLMs的模型库将任务分配给专家智能体；（2）验证机制，通过内部一致性检查以及利用病理学知识库和领域专用模型进行外部验证来确保准确性；（3）汇总模块，结合视觉解释图合成最终摘要。在多模态WSI基准上的大量实验表明，WSI-Agents在多种任务上优于当前的WSI MLLMs和医学智能体框架。

英文摘要：

Whole slide images (WSIs) are vital in digital pathology, enabling gigapixel tissue analysis across various pathological tasks. While recent advancements in multi-modal large language models (MLLMs) allow multi-task WSI analysis through natural language, they often underperform compared to task-specific models. Collaborative multi-agent systems have emerged as a promising solution to balance versatility and accuracy in healthcare, yet their potential remains underexplored in pathology-specific domains. To address these issues, we propose WSI-Agents, a novel collaborative multi-agent system for multi-modal WSI analysis. WSI-Agents integrates specialized functional agents with robust task allocation and verification mechanisms to enhance both task-specific accuracy and multi-task versatility through three components: (1) a task allocation module assigning tasks to expert agents using a model zoo of patch and WSI level MLLMs, (2) a verification mechanism ensuring accuracy through internal consistency checks and external validation using pathology knowledge bases and domain-specific models, and (3) a summary module synthesizing the final summary with visual interpretation maps. Extensive experiments on multi-modal WSI benchmarks show WSI-Agents's superiority to current WSI MLLMs and medical agent frameworks across diverse tasks.

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。