← 研踪 PaperTrailarXiv:2611.13168 · v1
RadRef: A Reference-Free Metric for Radiology Report Generation
首次提交 2026-11-10 03:53
版本更新 2026-11-10 03:53
雷达观测 2026-11-11 11:04
摘要
AI-generated radiology reports are usually evaluated by comparison with a radiologist-authored reference, leaving no way to score them when no reference exists. We introduce RadRef, a reference-free metric that scores report claims against image-derived beliefs over a clinical finding vocabulary, weighted by relevance to the clinical indication. On ReXVal, RadRef achieves 73.1% agreement on non-tied same-radiograph report pairs with radiologist rankings, comparable to leading reference-based metrics overall. It is also competitive on false findings and has its clearest relative result on omissions (missed findings). We further evaluate 100 model-generated and 100 radiologist-authored reports from 100 MIMIC-CXR studies, all independently annotated by two board-certified radiologists. On omissions, RadRef attains Kendall's tau-b = 0.295, against 0.386 between the two readers themselves, and no compared metric differs significantly from RadRef on this target. The same image-grounded representation provides a false-finding readout, RadRef-FF, with tau-b = 0.32 on ReXVal and 0.29 in the reader study, without requiring a reference. Controlled perturbations confirm that RadRef depends on the radiograph, report, and clinical indication. RadRef enables image-grounded evaluation before a radiologist-authored reference exists, supporting quality monitoring of report-generation systems in clinical settings, and complements reference-based metrics when one is available.
观测到的版本
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v2 · 2026-11-12 19:41 · 最新观测版本
RadRef: A Reference-Free Metric for Radiology Report Generation
AI-generated radiology reports are commonly evaluated against radiologist-authored references, limiting the applicability of standard reference-based metrics when no reference is available. We introduce RadRef, a reference-free metric that scores report claims against image-derived beliefs over a clinical finding vocabulary, weighted by relevance to the clinical indication. By evaluating findings beyond the report's stated claims, RadRef assesses omissions as well as unsupported assertions. On ReXVal, RadRef achieves $73.1\%$ agreement with radiologist rankings among non-tied same-radiograph report pairs, with $61.8\%$ pair coverage and $64.3\%$ tie-inclusive agreement. Its non-tied agreement is close to GREEN ($73.9\%$) and RadFact ($72.6\%$), with its strongest relative results on omissions. We further evaluate $100$ model-generated and $100$ radiologist-authored reports from $100$ MIMIC-CXR studies, independently annotated by two board-certified radiologists. On same-image omission rankings, RadRef attains Kendall's $τ_b = 0.295$, compared with $0.386$ between the readers. In separate paired comparisons across generated reports, no evaluated comparator significantly outperforms RadRef on omissions. The same image-grounded representation provides a false-finding readout, RadRef-FF, with $τ_b = 0.32$ on ReXVal and $0.29$ in the reader study. Controlled perturbations confirm that RadRef depends on the radiograph, report, and clinical indication. RadRef enables image-grounded evaluation before a radiologist-authored reference exists, supporting quality monitoring of report-generation systems in clinical settings, and complements reference-based metrics when one is available.
查看此版本解读 →查看此版本原文 ↗v1 · 2026-11-10 03:53
RadRef: A Reference-Free Metric for Radiology Report Generation
AI-generated radiology reports are usually evaluated by comparison with a radiologist-authored reference, leaving no way to score them when no reference exists. We introduce RadRef, a reference-free metric that scores report claims against image-derived beliefs over a clinical finding vocabulary, weighted by relevance to the clinical indication. On ReXVal, RadRef achieves 73.1% agreement on non-tied same-radiograph report pairs with radiologist rankings, comparable to leading reference-based metrics overall. It is also competitive on false findings and has its clearest relative result on omissions (missed findings). We further evaluate 100 model-generated and 100 radiologist-authored reports from 100 MIMIC-CXR studies, all independently annotated by two board-certified radiologists. On omissions, RadRef attains Kendall's tau-b = 0.295, against 0.386 between the two readers themselves, and no compared metric differs significantly from RadRef on this target. The same image-grounded representation provides a false-finding readout, RadRef-FF, with tau-b = 0.32 on ReXVal and 0.29 in the reader study, without requiring a reference. Controlled perturbations confirm that RadRef depends on the radiograph, report, and clinical indication. RadRef enables image-grounded evaluation before a radiologist-authored reference exists, supporting quality monitoring of report-generation systems in clinical settings, and complements reference-based metrics when one is available.
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