In the field of medical image analysis, the scarcity of Chinese chest X-ray report datasets has hindered the development of technology for generating Chinese chest X-ray reports. On one hand, the construction of a Chinese chest X-ray report dataset is limited by the time-consuming and costly process of accurate expert disease annotation. On the other hand, a single natural language generation metric is commonly used to evaluate the similarity between generated and ground-truth reports, while the clinical accuracy and effectiveness of the generated reports rely on an accurate disease labeler (classifier). To address the issues, this study proposes a disease labeler tailored for the generation of Chinese chest X-ray reports. This labeler leverages a dual BERT architecture to handle diagnostic reports and clinical information separately and constructs a hierarchical label learning algorithm based on the affiliation between diseases and body parts to enhance text classification performance. Utilizing this disease labeler, a Chinese chest X-ray report dataset comprising 51,262 report samples was established. Finally, experiments and analyses were conducted on a subset of expert-annotated Chinese chest X-ray reports, validating the effectiveness of the proposed disease labeler.

该研究提出了一种专门用于生成中文胸部X射线报告的疾病标定器，该标定器利用双BERT架构处理诊断报告和临床信息并基于疾病和身体部位的关联构建了一个分层标签学习算法，从而提高了文本分类性能。利用该疾病标定器，建立了一个包含51,262个报告样本的中文胸部X射线报告数据集。最后，对一部分专家注释的中文胸部X射线报告进行了实验证明了该疾病标定器的有效性。

中文胸部X射线报告生成的疾病标签器