用细粒度文本片段自动评估中文放射报告质量，减少医生负担。
Weakly Supervised Fine-grained Span-Level Framework for Chinese Radiology Report Quality Assurance
- 通过分析初稿与修改稿间细粒度文本片段差异来评分
- 在12,013份报告上达到与专家评分相当的准确率
- 可解释性强，片段重要性得分符合资深医生判断
放射科报告质量保障（QA）旨在判断住院医师撰写的报告是否合格。该过程由资深医生结合影像和报告进行评审并打分，耗时耗力，且易受诊断偏见、医生能力等因素影响。为解决此问题，本文提出一种细粒度段级质量评估框架 Sqator，通过分析初稿与修订稿之间的语义差异片段来自动打分。不同于传统的文档级语义比较方法，Sqator 重点识别并量化关键修订片段的重要性，并融合所有片段得分生成最终评分。在包含12,013份放射报告的数据集上实验表明，Sqator 可实现与人工评分相当的性能，且各修订片段的重要程度得分与资深医生判断高度一致。
原文摘要 · Abstract (English)
Quality Assurance (QA) for radiology reports refers to judging whether the junior reports (written by junior doctors) are qualified. The QA scores of one junior report are given by the senior doctor(s) after reviewing the image and junior report. This process requires intensive labor costs for senior doctors. Additionally, the QA scores may be inaccurate for reasons like diagnosis bias, the ability of senior doctors, and so on. To address this issue, we propose a Span-level Quality Assurance EvaluaTOR (Sqator) to mark QA scores automatically. Unlike the common document-level semantic comparison method, we try to analyze the semantic difference by exploring more fine-grained text spans. Specifically, Sqator measures QA scores by measuring the importance of revised spans between junior and senior reports, and outputs the final QA scores by merging all revised span scores. We evaluate Sqator using a collection of 12,013 radiology reports. Experimental results show that Sqator can achieve competitive QA scores. Moreover, the importance scores of revised spans can be also consistent with the judgments of senior doctors.
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