基于语义一致性的不确定性量化用于放射学报告生成的事实性
Semantic Consistency-Based Uncertainty Quantification for Factuality in Radiology Report Generation
AI总结：
本文提出即插即用的语义一致性不确定性量化框架，从报告级和句子级识别放射学报告幻觉，在 MIMIC-CXR 上显著提升 Radialog 生成报告的事实性。
摘要：
放射学报告生成（RRG）通过自动化劳动密集型的报告撰写任务，在协助放射科医生方面展现出巨大潜力。尽管近期进展提升了生成报告的质量和连贯性，但确保其事实正确性仍是一项关键挑战。虽然已提出用于解决该问题的生成式医学视觉大语言模型（VLLMs），但这些模型容易产生幻觉，并可能生成不准确的诊断信息。为解决这些问题，我们提出一种新颖的基于语义一致性的不确定性量化框架，可同时提供报告级和句子级不确定性。与现有方法不同，我们的方法无需修改底层模型，也无需访问其内部状态（例如输出 token logits），因此可作为即插即用模块，与最先进模型无缝集成。大量实验证明了我们的方法在检测幻觉和提升自动生成放射学报告事实准确性方面的有效性。通过对高不确定性报告弃权（不执行），我们的方法将事实性分数提高了10%；这一结果是在 MIMIC-CXR 数据集上使用 Radialog 模型拒绝20%的报告实现的。此外，句子级不确定性以82.9%的成功率标记每份报告中精确率最低的句子。我们的实现已开源，可在 https://github.com/BU-DEPEND-Lab/SCUQ-RRG 获取。
英文摘要：
Radiology report generation (RRG) has shown great potential in assisting radiologists by automating the labor-intensive task of report writing. While recent advancements have improved the quality and coherence of generated reports, ensuring their factual correctness remains a critical challenge. Although generative medical Vision Large Language Models (VLLMs) have been proposed to address this issue, these models are prone to hallucinations and can produce inaccurate diagnostic information. To address these concerns, we introduce a novel Semantic Consistency-Based Uncertainty Quantification framework that provides both report-level and sentence-level uncertainties. Unlike existing approaches, our method does not require modifications to the underlying model or access to its inner state, such as output token logits, thus serving as a plug-and-play module that can be seamlessly integrated with state-of-the-art models. Extensive experiments demonstrate the efficacy of our method in detecting hallucinations and enhancing the factual accuracy of automatically generated radiology reports. By abstaining from high-uncertainty reports, our approach improves factuality scores by $10$\%, achieved by rejecting $20$\% of reports using the \texttt{Radialog} model on the MIMIC-CXR dataset. Furthermore, sentence-level uncertainty flags the lowest-precision sentence in each report with an $82.9$\% success rate. Our implementation is open-source and available at https://github.com/BU-DEPEND-Lab/SCUQ-RRG.
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。
