arXiv cs.AI· Chentao Huang, Runlong Lin, Zhijun Wang, Yunxiang Li, Yuanyuan Sun, Jian Wang, Hongfei Lin, Ling Luo·· 3 小时前AI 评分38
ARIVE 提出解剖检查与诊断推理交错的可解释放射报告生成框架
Interleaving anatomical inspection and diagnostic reasoning for explainable radiology report generation
AI 导读
研究者提出 ARIVE（Anatomy-guided Reasoning with Interleaved Visual Evidence），在生成报告前交错进行解剖区域检查与诊断推理，使放射报告可解释、可追溯。
正文 · 原文
Abstract:Automated radiology report generation can draft clinically plausible reports, yet the pathway from visual evidence to reported findings remains opaque, leaving the diagnostic basis of these reports unexplained and the generated reports difficult to review or revise. We introduce ARIVE (Anatomy-guided Reasoning with Interleaved Visual Evidence), an explainable report generation framework that interleaves anatomical inspection with diagnostic reasoning prior to final report composition. To support this process, we constructed 159,416 multimodal chain-of-thought (MCoT) trajectories from MIMIC-CXR, wherein visual evidence from inspected anatomical regions informs subsequent clinical interpretation. Trained on these trajectories and further optimized using anatomical and clinical-content rewards, ARIVE achieved superior clinical-efficacy performance compared with strong baselines on MIMIC-CXR and the abnormality-centered MIMIC-ABN. ARIVE's reports were competitive with those of leading models in blinded expert grading, and its MCoT trajectories earned high ratings for diagnostic traceability. Furthermore, a paired review evaluation suggests that access to MCoT can aid in identifying errors within generated reports.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.14018 [cs.CV] |
| (or arXiv:2611.14018v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2611.14018 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Chentao Huang [view email]
[v1]
Tue, 10 Nov 2026 12:52:23 UTC (26,362 KB)
来源：arXiv cs.AI · arxiv.org