  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.
