面向可靠胎儿超声解读的多智能体协作
Towards Reliable Fetal Ultrasound Interpretation with Multi-Agent Collaboration
AI总结：
提出FetUSAgents多智能体系统，通过协作LLM代理和双路径证据仲裁（DPEA）整合视觉工具与临床推理，在胎儿超声VQA、报告生成等任务上超越最强基线25%以上。
摘要：
自动化胎儿超声解读需要从视觉感知（包括平面识别和解剖分割）到临床理解（包括生物测量和诊断报告）的工作流程。然而，当前“一任务一模型”的范式限制了跨多步骤过程的系统性证据整合。尽管多模态大语言模型（MLLM）展现出有前景的视觉理解能力，但其有限的领域特定基础和幻觉风险限制了在胎儿超声分析中的可靠性。为解决这些限制，我们提出了FetUSAgents，一个工具增强的多智能体系统，用于全面的胎儿超声解读，支持视觉问答（VQA）、报告生成、图像描述和视频总结。FetUSAgents通过协作的LLM代理协调任务特定的视觉工具，并将临床查询分解为从解剖识别到定量测量的子任务。我们进一步引入了双路径证据仲裁（DPEA），它将基于LLM的审慎推理与来自专业视觉工具的结构化计算证据相结合。一个检索增强的证据库整合中间发现，以支持可追溯且临床可靠的结论。此外，我们构建了FetUS-VQA，一个专门用于胎儿超声的VQA基准，包含1,892张图像和3,205个问答对，涵盖10个临床任务。广泛的分布外实验表明，FetUSAgents优于通用和医学MLLM，在VQA准确率上超过最强基线25%以上。这些结果表明了一条通往产前成像的基于证据的临床助手的可扩展路径。代码已公开。
英文摘要：
Automated fetal ultrasound interpretation requires a workflow from visual perception, including plane recognition and anatomical segmentation, to clinical understanding, including biometric measurement and diagnostic reporting. However, the prevailing "one-task, one-model" paradigm limits systematic integration of evidence across this multi-step process. Although multimodal large language models (MLLMs) show promising visual understanding, their limited domain-specific grounding and hallucination risks restrict reliability in fetal ultrasound analysis. To address these limitations, we propose FetUSAgents, a tool-augmented multi-agent system for comprehensive fetal ultrasound interpretation, supporting visual question answering (VQA), report generation, image captioning, and video summarization. FetUSAgents coordinates task-specific visual tools through collaborative LLM agents and decomposes clinical queries into subtasks that progress from anatomical recognition to quantitative measurement. We further introduce Dual-Path Evidence Arbitration (DPEA), which integrates LLM-based deliberative reasoning with structured computational evidence from specialized visual tools. A retrieval-enhanced evidence bank consolidates intermediate findings to support traceable and clinically grounded conclusions. In addition, we construct FetUS-VQA, a dedicated VQA benchmark for fetal ultrasound, comprising 1,892 images and 3,205 question-answer pairs across 10 clinical tasks. Extensive out-of-distribution experiments show that FetUSAgents outperforms general and medical MLLMs, exceeding the strongest baseline by more than 25 percent in VQA accuracy. These results suggest a scalable route toward evidence-driven clinical assistants for prenatal imaging. Code is available.
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