Radiology report summarization is a crucial task that can help doctors
quickly identify clinically significant findings without the need to review
detailed sections of reports. This study proposes RadBARTsum, a domain-specific
and ontology facilitated adaptation of the BART model for abstractive radiology
report summarization. The approach involves two main steps: 1) re-training the
BART model on a large corpus of radiology reports using a novel entity masking
strategy to improving biomedical domain knowledge learning, and 2) fine-tuning
the model for the summarization task using the Findings and Background sections
to predict the Impression section. Experiments are conducted using different
masking strategies. Results show that the re-training process with domain
knowledge facilitated masking improves performances consistently across various
settings. This work contributes a domain-specific generative language model for
radiology report summarization and a method for utilising medical knowledge to
realise entity masking language model. The proposed approach demonstrates a
promising direction of enhancing the efficiency of language models by deepening
its understanding of clinical knowledge in radiology reports.

该研究提出了 RadBARTsum，一种领域特定的、本体学支持的适应 BART 模型的放射学报告摘要方法。该方法包括两个主要步骤：1）使用一种新颖的实体屏蔽策略在大量放射学报告语料库上重新训练 BART 模型以提高生物医学领域知识学习，2）使用调查结果和背景部分进行模型微调以预测结论部分。实验采用不同的屏蔽策略进行，结果显示使用领域知识屏蔽的重新训练过程在各种设置下均能持续提高性能。该工作为放射学报告摘要提供了一种领域特定生成型语言模型，以及一种利用医学知识实现实体屏蔽语言模型的方法。所提出的方法展示了通过加深对放射学报告的临床知识理解来提高语言模型效率的一个有希望的方向。