论文标题
显示，描述和结论：利用胸部X射线报告的结构信息
Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports
论文作者
论文摘要
胸部X射线（CXR）图像通常用于临床筛查和诊断。自动编写这些图像的报告可以大大减轻放射科医生的工作量，以总结描述性发现和确定的印象。报告之间和内部之间的复杂结构对自动报告的生成构成了巨大挑战。具体而言，本节印象是对本节发现的诊断摘要。正态性的出现主导了每个部分的异常。现有研究很少探索并考虑此基本结构信息。在这项工作中，我们提出了一个新颖的框架，该框架利用报告部分和内部的结构信息来生成CXR成像报告。首先，我们提出了一种两阶段的策略，该策略明确地模拟了发现与印象之间的关系。其次，我们设计了一种新型的合作多代理系统，该系统隐含地捕获了异常和正常性之间的不平衡分布。两个CXR报告数据集的实验表明，我们的方法在各种评估指标方面实现了最先进的性能。我们的结果表明，所提出的方法能够通过整合结构信息来产生高质量的医疗报告。
Chest X-Ray (CXR) images are commonly used for clinical screening and diagnosis. Automatically writing reports for these images can considerably lighten the workload of radiologists for summarizing descriptive findings and conclusive impressions. The complex structures between and within sections of the reports pose a great challenge to the automatic report generation. Specifically, the section Impression is a diagnostic summarization over the section Findings; and the appearance of normality dominates each section over that of abnormality. Existing studies rarely explore and consider this fundamental structure information. In this work, we propose a novel framework that exploits the structure information between and within report sections for generating CXR imaging reports. First, we propose a two-stage strategy that explicitly models the relationship between Findings and Impression. Second, we design a novel cooperative multi-agent system that implicitly captures the imbalanced distribution between abnormality and normality. Experiments on two CXR report datasets show that our method achieves state-of-the-art performance in terms of various evaluation metrics. Our results expose that the proposed approach is able to generate high-quality medical reports through integrating the structure information.
