通过在大语言模型中段训练改进放射报告的自动摘要
Improving Automatic Summarization of Radiology Reports through Mid-Training of Large Language Models
AI总结：
本文提出在大语言模型中段训练的方法，通过三种策略提升放射报告摘要效果，实验表明GatorTronT5-Radio在文本和事实性指标上表现最佳，且在少样本学习中表现更优。
摘要：
自动摘要放射报告是减轻医生负担的重要应用。先前研究广泛采用
英文摘要：
Automatic summarization of radiology reports is an essential application to reduce the burden on physicians. Previous studies have widely used the "pre-training, fine-tuning" strategy to adapt large language models (LLMs) for summarization. This study proposed a subdomain adaptation through a mid-training method to improve summarization. We explored three adaptation strategies: (1) general-domain pre-training, (2) clinical-domain pre-training, and (3) clinical-domain pre-training followed by subdomain mid-training. We developed models using large-scale clinical text from the University of Florida (UF) Health and conducted mid-training and fine-tuning experiments using widely used benchmark datasets including OpenI and MIMIC-CXR. The experimental results show that the mid-trained model, GatorTronT5-Radio, achieved the best performance, outperforming models without mid-training in both text-based measures (ROUGE-L) and factuality measures (RadGraph-F1). Our mid-training methods also demonstrate better few-shot learning and could alleviate the "cold start" problem reported in previous studies as a learning barrier. Our findings support the use of "pre-training, mid-training, fine-tuning," instead of the widely used direct fine-tuning strategy.
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。
