通过临床发现的细粒度短语接地评估自动化放射学报告质量
Evaluating Automated Radiology Report Quality through Fine-Grained Phrasal Grounding of Clinical Findings
AI总结：
本文提出一种结合细粒度临床发现提取与图像短语接地的新评估方法，用于自动评估胸部X光报告质量，并在MIMIC数据集上验证了其鲁棒性和对事实错误的敏感性。
摘要：
近年来，基于文本信息，利用词汇、语义或临床命名实体识别方法，已开发出多种评估指标，用于自动评估胸部X光片生成式AI报告的质量。本文提出了一种新的报告质量评估方法：首先提取细粒度的发现模式，涵盖大量临床发现的位置、偏侧性和严重程度；随后进行短语接地，在胸部X光图像上定位其相关的解剖区域；最后结合文本与视觉度量，对生成报告的质量进行评级。我们展示了在源自MIMIC集合的金标准数据集上，将该评估指标与其他文本指标进行比较的结果，并证明了其对事实错误的鲁棒性和敏感性。
英文摘要：
Several evaluation metrics have been developed recently to automatically assess the quality of generative AI reports for chest radiographs based only on textual information using lexical, semantic, or clinical named entity recognition methods. In this paper, we develop a new method of report quality evaluation by first extracting fine-grained finding patterns capturing the location, laterality, and severity of a large number of clinical findings. We then performed phrasal grounding to localize their associated anatomical regions on chest radiograph images. The textual and visual measures are then combined to rate the quality of the generated reports. We present results that compare this evaluation metric with other textual metrics on a gold standard dataset derived from the MIMIC collection and show its robustness and sensitivity to factual errors.
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。
