RA-RRG：结合关键短语提取的多模态检索增强放射学报告生成
RA-RRG: Multimodal Retrieval-Augmented Radiology Report Generation with Key Phrase Extraction
AI总结：
RA-RRG通过结合多模态检索与大语言模型生成放射学报告，减少幻觉和计算需求，实验显示在CheXbert指标上优于现有模型。
摘要：
自动放射学报告生成（RRG）有望减轻放射科医生的工作负担，而多模态大语言模型（MLLMs）的进展使多模态胸部X光报告生成成为可能。然而，现有MLLMs计算成本高、需要大规模训练数据且可能产生幻觉，限制了实际部署。为此，我们提出RA-RRG，一种结合多模态检索与大语言模型（LLMs）的RRG框架，通过提取放射学报告中的关键短语并根据输入图像检索相关短语，从而有效抑制幻觉并保持强大的报告生成性能。在MIMIC-CXR和IU X光数据集上的实验显示，RA-RRG在CheXbert指标上达到SOTA结果，在RadGraph F1分数上与MLLMs竞争。此外，RA-RRG自然扩展到多视图RRG，通过聚合多个图像检索的短语，突显其在真实临床场景中的广泛应用潜力。代码可在https://github.com/deepnoid-ai/RA-RRG获取。
英文摘要：
Automated radiology report generation (RRG) holds potential to reduce the workload of radiologists, and recent advances in multimodal large language models (MLLMs) have enabled multimodal chest X-ray (CXR) report generation. However, existing MLLMs are computationally expensive, require large-scale training data, and may produce hallucinated content, limiting their practical deployment. To address these limitations, we propose RA-RRG, a retrieval-augmented RRG framework that combines multimodal retrieval with large language models (LLMs) to generate radiology reports while reducing hallucinations and computational demands. RA-RRG uses LLMs to extract clinically essential key phrases from radiology reports and retrieves relevant phrases given an input image. By conditioning LLMs on the retrieved phrases, RA-RRG effectively suppresses hallucinations while maintaining strong report generation performance. Experiments on the MIMIC-CXR and IU X-ray datasets show state-of-the-art results on CheXbert metrics and competitive RadGraph F1 scores compared to MLLMs. Furthermore, RA-RRG naturally generalizes to multi-view RRG by aggregating phrases retrieved from multiple images, highlighting its broad applicability to real-world clinical scenarios. Code is available at https://github.com/deepnoid-ai/RA-RRG.
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。
