A Disease Labeler for Chinese Chest X-Ray Report Generation
Wang, Mengwei, Yan, Ruixin, Hou, Zeyi, Lang, Ning, Zhou, Xiuzhuang
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
Mar-18-2024
- Country:
- Asia (0.29)
- North America > United States (0.46)
- Genre:
- Research Report (0.83)
- Industry:
- Health & Medicine
- Diagnostic Medicine > Imaging (1.00)
- Nuclear Medicine (0.98)
- Health & Medicine
- Technology: