计算机科学与探索2024，Vol.18Issue(9)：2337-2348,12.DOI:10.3778/j.issn.1673-9418.2406041
生成式大语言模型在中文放射医学领域的应用研究
Application of Generative Large Language Models in Chinese Radiology Domain
陈龙飞 1高鑫 1侯皓天 1叶初阳 2刘亚欧 3张美慧1
作者信息
- 1. 北京理工大学 计算机学院,北京 100081
- 2. 北京理工大学 集成电路与电子学院,北京 100081
- 3. 首都医科大学附属北京天坛医院 放射科,北京 100070
- 折叠
摘要
Abstract
In the Chinese radiology domain,radiology reports serve as a crucial basis for clinical decision-making.Therefore,utilizing natural language processing(NLP)technology to understand and learn from the textual content of radiology reports,thereby aiding radiological clinical work,has become an important research direction in this domain.However,when dealing with the natural language classification and generation tasks based on Chinese radiology reports using traditional methods,there are still challenges such as a lack of training corpora,privacy concerns,and poor model generalization capabilities,leading to insufficient overall performance.To address these issues,a solution for natural language tasks in the Chinese radiology domain based on locally efficient fine-tuning large language models is proposed.By collecting and constructing a large-scale,high-quality dataset for natural language tasks in the Chinese radiology reports,and employing the LoRA efficient fine-tuning method for supervised fine-tuning training of the open-source large language model Baichuan2,the"RadGPT"capable of solving four types of clinical tasks in the Chinese radiology domain simultaneously is proposed.A set of evaluation systems for natural language classification and generation tasks in the Chinese radiology domain is introduced.Multiple sets of experiments are conducted on three types of radiology report datasets from two centers,and comparisons are made with several typical existing methods.The results demonstrate that the proposed method performs better in terms of classification performance,text summarization and expansion capabilities,and model generalization.关键词
大语言模型/影像学报告/文本分类/文本生成/高效微调策略Key words
large language model/radiology report/text classification/text generation/efficient fine-tuning strategy分类
信息技术与安全科学引用本文复制引用
陈龙飞,高鑫,侯皓天,叶初阳,刘亚欧,张美慧..生成式大语言模型在中文放射医学领域的应用研究[J].计算机科学与探索,2024,18(9):2337-2348,12.
