用于中文胸片报告生成的疾病标注器
A Disease Labeler for Chinese Chest X-Ray Report Generation
AI总结：
针对中文胸片报告数据集稀缺及评估缺乏临床准确性等问题，提出基于双BERT架构和分层标签学习的疾病标注器，并构建了含51,262个样本的数据集，验证了其有效性。
摘要：
在医学图像分析领域，中文胸片报告数据集的稀缺阻碍了中文胸片报告生成技术的发展。一方面，中文胸片报告数据集的构建受限于耗时且成本高昂的准确专家疾病标注过程。另一方面，通常使用单一的自然语言生成指标来评估生成报告与真实报告之间的相似度，而生成报告的临床准确性和有效性则依赖于准确的疾病标注器（分类器）。为了解决这些问题，本研究提出了一种专为中文胸片报告生成定制的疾病标注器。该标注器利用双 BERT 架构分别处理诊断报告和临床信息，并基于疾病与身体部位之间的隶属关系构建了分层标签学习算法，以增强文本分类性能。利用该疾病标注器，建立了一个包含 51,262 个报告样本的中文胸片报告数据集。最后，在专家标注的中文胸片报告子集上进行了实验和分析，验证了所提出的疾病标注器的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
