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首页|期刊导航|中国中西医结合影像学杂志|基于有监督微调的大语言模型利用胸部CT报告中的描述自动生成印象的初步研究

基于有监督微调的大语言模型利用胸部CT报告中的描述自动生成印象的初步研究

杨灵睿 杨学东 齐俊 周昱行 甄先通 史珊 孙黎 苏清华

中国中西医结合影像学杂志2025,Vol.23Issue(3):358-362,5.
中国中西医结合影像学杂志2025,Vol.23Issue(3):358-362,5.DOI:10.3969/j.issn.1672-0512.2025.03.017

基于有监督微调的大语言模型利用胸部CT报告中的描述自动生成印象的初步研究

Study on automatic generation of impressions from chest CT reports using supervised fine-tuning large language models

杨灵睿 1杨学东 1齐俊 2周昱行 2甄先通 2史珊 1孙黎 1苏清华3

作者信息

  • 1. 中国中医科学院广安门医院放射科,北京 100053
  • 2. 北京联影智能影像技术研究院,北京 100089
  • 3. 北京物资学院信息学院,北京 101149
  • 折叠

摘要

Abstract

Objective:To explore the application for automatic generation of impressions from chest CT reports using supervised fine-tuning(SFT)large language models(LLMs).Methods:A retrospective study was conducted,collecting textual reports from 140,000 chest CT reports.After data cleaning,a data set was constructed from 75,884 reports.72,000 reports were taken as the training set for SFT training,and 3,844 reports were taken as the validation set for different LLMs and fine-tuning strategies.The performance of different LLMs and fine-tuning strategies was compared to identify the optimal model and tuning technique.The selected LLM was then trained using SFT.Finally,the accuracy and quality of impression generated by the original and fine-tuned models were compared.Natural language metrics,such as ROUGE and BLEU,were used for performance evaluation.Results:In basic models,the Baichuan2-13B model achieved BLEU-4 of 51.82 points,ROUGE-1 of 69.69 points,ROUGE-2 of 52.40 points,and ROUGE-L of 63.59 points.In ChatGLM-6B model,the LoRA method achieved ROUGE-1 of 64.04 points,ROUGE-2 of 42.39 points,ROUGE-L of 57.51 points,and BLEU-4 of 44.29 points.Both of them demonstrated superior performance and were selected for SFT training,and after training,the output quality of impressions by the LLM showed significant improvement in quantitative metrics.Conclusions:SFT-LLM demonstrats remarkable performance enhancements in generating impressions for clinical chest CT reports,producing results comparable to those of clinical practitioners.This highlights their potential for application in the domain of medical imaging report generation.

关键词

有监督微调/大语言模型/胸部CT报告生成

Key words

Supervised fine-tuning/Large language model/Chest CT reports generation

引用本文复制引用

杨灵睿,杨学东,齐俊,周昱行,甄先通,史珊,孙黎,苏清华..基于有监督微调的大语言模型利用胸部CT报告中的描述自动生成印象的初步研究[J].中国中西医结合影像学杂志,2025,23(3):358-362,5.

基金项目

中国中医科学院科技创新工程项目(CI2021A03302). (CI2021A03302)

中国中西医结合影像学杂志

1672-0512

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