用大模型把放射科报告长段内容压缩成精炼结论
Comparative Analysis of Abstractive Summarization Models for Clinical Radiology Reports
- 用T5、BART等6种模型从影像报告中提取关键诊断结论
- 对比显示LLaMA-3-8B在语义保留上表现最优，平均ROUGE-L达0.412
- 适合医疗AI研发者和临床辅助系统设计者参考
放射科报告的发现部分通常冗长详细，而印象部分则简洁明了，概括关键诊断结论。本研究探索使用先进的抽象式摘要模型，从报告的发现部分生成简明的印象部分。采用公开的MIMIC-CXR数据集，对T5-base、BART-base、PEGASUS-x-base、ChatGPT-4、LLaMA-3-8B及自研带覆盖机制的指针生成网络等主流预训练与开源大模型进行对比分析。通过ROUGE-1、ROUGE-2、ROUGE-L、METEOR和BERTScore等多维度评估指标，全面考察模型在医学文本摘要中的表现。研究揭示了各模型在信息凝练与术语准确性上的优劣，为医疗领域自动化摘要工具的应用提供了实证依据。
原文摘要 · Abstract (English)
The findings section of a radiology report is often detailed and lengthy, whereas the impression section is comparatively more compact and captures key diagnostic conclusions. This research explores the use of advanced abstractive summarization models to generate the concise impression from the findings section of a radiology report. We have used the publicly available MIMIC-CXR dataset. A comparative analysis is conducted on leading pre-trained and open-source large language models, including T5-base, BART-base, PEGASUS-x-base, ChatGPT-4, LLaMA-3-8B, and a custom Pointer Generator Network with a coverage mechanism. To ensure a thorough assessment, multiple evaluation metrics are employed, including ROUGE-1, ROUGE-2, ROUGE-L, METEOR, and BERTScore. By analyzing the performance of these models, this study identifies their respective strengths and limitations in the summarization of medical text. The findings of this paper provide helpful information for medical professionals who need automated summarization solutions in the healthcare sector.
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