用解剖结构指导的模型检测肺部X光报告中的事实错误。
Anatomically-Grounded Fact Checking of Automated Chest X-ray Reports
- 基于解剖位置设计跨模态对比回归网络，定位报告错误。
- 在多个基准数据集上提升报告质量超40%。
- 适合医学AI验证、临床辅助诊断系统开发者。
随着大规模视觉-语言模型的发展，仅凭医学影像即可生成逼真的放射科报告。然而，这些报告常存在事实性错误，限制了其实际应用。本文提出一种可解释的事实核查模型，能够识别报告中发现及其位置的错误。我们分析了自动化报告方法的错误类型，并基于真实数据集构建了一个包含真实与虚假描述及位置的新合成数据集。在此基础上训练了一种新的多标签跨模态对比回归网络。在多个基准数据集上的评估表明，该模型能有效检测并修正多个先进自动化报告工具生成的报告，使报告质量提升超过40%。
原文摘要 · Abstract (English)
With the emergence of large-scale vision-language models, realistic radiology reports may be generated using only medical images as input guided by simple prompts. However, their practical utility has been limited due to the factual errors in their description of findings. In this paper, we propose a novel model for explainable fact-checking that identifies errors in findings and their locations indicated through the reports. Specifically, we analyze the types of errors made by automated reporting methods and derive a new synthetic dataset of images paired with real and fake descriptions of findings and their locations from a ground truth dataset. A new multi-label cross-modal contrastive regression network is then trained on this datsaset. We evaluate the resulting fact-checking model and its utility in correcting reports generated by several SOTA automated reporting tools on a variety of benchmark datasets with results pointing to over 40\% improvement in report quality through such error detection and correction.
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