Wang Zhichuan, Deng Qiao, So Tiffany Y, Chiu Wan Hang, Lee Kinhei, Hui Edward S
Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, HKSAR, China.
The CU Lab for AI in Radiology (CLAIR), The Chinese University of Hong Kong, HKSAR, China.
Sci Rep. 2025 Jul 24;15(1):26930. doi: 10.1038/s41598-025-12684-2.
A chest X-ray radiology report describes abnormal findings not only from X-ray obtained at a given examination, but also findings on disease progression or change in device placement with reference to the X-ray from previous examination. Majority of the efforts on automatic generation of radiology report pertain to reporting the former, but not the latter, type of findings. To the best of the authors' knowledge, there is only one work dedicated to generating summary of the latter findings, i.e., follow-up radiology report summary. In this study, we propose a transformer-based framework to tackle this task. Motivated by our observations on the significance of medical lexicon on the fidelity of report summary generation, we introduce two mechanisms to bestow clinical insight to our model, namely disease probability soft guidance and masked entity modeling loss. The former mechanism employs a pretrained abnormality classifier to guide the presence level of specific abnormalities, while the latter directs the model's attention toward medical lexicon. Extensive experiments were conducted to demonstrate that the performance of our model exceeded the state-of-the-art.
一份胸部X光放射学报告不仅描述了在特定检查中获得的X光的异常发现，还参照先前检查的X光描述了疾病进展或设备放置变化的发现。自动生成放射学报告的大部分工作都致力于报告前一种发现，而不是后一种发现。据作者所知，只有一项工作致力于生成后一种发现的总结，即随访放射学报告总结。在本研究中，我们提出了一个基于Transformer的框架来解决这个任务。基于我们对医学词汇对报告总结生成保真度的重要性的观察，我们引入了两种机制来赋予我们的模型临床洞察力，即疾病概率软引导和掩码实体建模损失。前一种机制采用预训练的异常分类器来指导特定异常的存在水平，而后一种机制将模型的注意力引向医学词汇。进行了广泛的实验以证明我们模型的性能超过了现有技术水平。
