Problem
Research questions and friction points this paper is trying to address.
Radiology Report Generation
Medical Hallucinations
Diagnostic Interpretability
Knowledge Fusion
Dynamic Knowledge Updating
MAC-RRG: Iterative Multi-Agent Collaboration for X-ray Radiology Report Generation
🤖 AI Summary
本文提出MAC-RRG框架，通过多智能体协作和迭代优化解决X射线报告生成中的医学幻觉与低解释性问题，结合结构化与非结构化知识指导模型逐步改进报告。
📝 Abstract
Despite the remarkable progress of LLM-based and knowledge graph-augmented Radiology Report Generation (RRG) methods, existing techniques still suffer from inherent defects. Conventional LLM-only models lack structured medical prior knowledge, resulting in frequent medical hallucinations and low diagnostic interpretability. Current knowledge graph-enhanced schemes adopt static one-round knowledge fusion with single-source knowledge, incapable of dynamic knowledge updating according to generation feedback. This paper proposes a novel Multi-Agent Collaborative iterative framework for X-ray Radiology Report Generation, termed MAC-RRG. Inspired by multi-agent technology, our framework constructs a closed-loop optimization paradigm based on task decoupling and collaborative reasoning. Specifically, the framework first generates a preliminary radiology report from input X-ray images via a vision encoder and a basic LLM. Subsequently, a multimodal knowledge graph (MM-KG) agent mines structured disease correlation and anatomical knowledge from medical knowledge graphs, while an auxiliary knowledge agent extracts unstructured domain knowledge from public medical databases. The multi-source knowledge acquired by dual agents is fused and embedded to guide the LLM in iteratively refining the initial report. Extensive quantitative and qualitative experiments on mainstream X-ray RRG datasets, including IU X-ray, MIMIC, and CheXpert Plus, fully verify the superiority of our proposed method. The source code and pre-trained models have been released on https://github.com/Event-AHU/Medical_Image_Analysis
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multi-Agent Collaboration
Iterative Refinement
Multimodal Knowledge Graph
Closed-loop Optimization
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