arXiv cs.CL· Jinwei Yang, Junhao Chen, Jianwei Zhao, Zhonghan Xu, Hong Cheng, Ling Wang·· 13 小时前AI 评分24
RadCarve：以解剖区域集合为生成单元，按证据关节切分放射学报告
Carving Radiology Reports at Their Evidential Joints: Anatomical Region Sets as Generation Units
AI 导读
RadCarve 提出直接从现有陈述-区域对应关系中的解剖区域集合定义生成单元，将单元划分与视觉证据选择统一起来。其 planner 用组合专用查询库从图像联合预测单元存在性与区域组成并迭代细化，生成器基于预测区域集合的联合视觉证据独立生成每个单元文本。
正文 · 原文
Abstract:Organizing radiology reports into independently generated units offers a way to generate fine-grained clinical content and bind it to visual evidence. However, existing methods typically adopt generation-unit structures not directly specified in natural reports or their annotations, introducing additional modeling compromises. We propose RadCarve, which instead defines generation units directly from the anatomical region sets in existing statement-region correspondences, unifying unit formation with visual evidence selection. RadCarve's planner uses composition-specialized query banks to jointly predict unit existence and region composition from the image, iteratively refining both with regional evidence aggregated according to current membership predictions. The generator independently produces each unit's text conditioned on the joint visual evidence of its predicted region set. Compared with strong baselines, RadCarve improves CXRFEScore by 9.4% in unit generation given region sets and achieves 2.4 times the end-to-end binding F1. It also achieves leading report-generation results on Chest ImaGenome and PadChest-GR, using fewer than one-sixth as many parameters as large-scale baselines.
| Comments: | 28 pages, 4 figures |
| Subjects: | Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2610.29165 [cs.CL] |
| (or arXiv:2610.29165v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.29165 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jinwei Yang [view email]
[v1]
Wed, 21 Oct 2026 03:55:22 UTC (7,299 KB)
来源：arXiv cs.CL · arxiv.org