专攻胸部X光骨折描述，提升罕见病报告准确性
ChexFract: From General to Specialized -- Enhancing Fracture Description Generation
- 用MAIRA-2和CheXagent构建骨折专用视觉语言模型
- 在骨折描述准确率上显著优于通用模型
- 公开最佳模型，助力罕见病影像报告研究
从胸部X光图像生成准确且具有临床意义的放射科报告仍是医学AI中的重大挑战。尽管近期视觉语言模型在通用放射科报告生成方面表现强劲，但对骨折等罕见但临床重要的病理仍描述不足。本文通过训练针对骨折病理的专用视觉语言模型，弥补这一差距。模型采用MAIRA-2和CheXagent的编码器，在不同骨折类型、位置和年龄下进行分析，揭示了当前视觉语言模型架构在描述上的优劣。实验表明，专用模型在骨折描述生成上显著优于通用模型。我们已公开表现最佳的骨折报告模型，推动罕见病理精准报告的研究发展。
原文摘要 · Abstract (English)
Generating accurate and clinically meaningful radiology reports from chest X-ray images remains a significant challenge in medical AI. While recent vision-language models achieve strong results in general radiology report generation, they often fail to adequately describe rare but clinically important pathologies like fractures. This work addresses this gap by developing specialized models for fracture pathology detection and description. We train fracture-specific vision-language models with encoders from MAIRA-2 and CheXagent, demonstrating significant improvements over general-purpose models in generating accurate fracture descriptions. Analysis of model outputs by fracture type, location, and age reveals distinct strengths and limitations of current vision-language model architectures. We publicly release our best-performing fracture-reporting model, facilitating future research in accurate reporting of rare pathologies.
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