AI 中文总结
本文针对放射学报告未指定分割目标的问题，提出EliSeg框架，通过提议-验证-修订机制实现报告驱动的异常分割，在MIMIC-CXR-ILS等数据集上性能优于现有方法。
AI 中文摘要
放射学报告描述临床观察，但未指定可执行的分割目标，可能包含阳性、阴性、既往、不确定或无关的发现，且可能共存多个有效异常。现有分割方法大多通过在推理前接收目标标识或空间提示来规避这种歧义，充当隐式目标 oracle。本文研究报告驱动的异常分割任务，要求模型从未过滤的报告中直接确定目标资格、基数及发现与掩码的对应关系，再描绘对应区域。我们提出EliSeg，一种“提议-验证-修订”框架，将目标构建与掩码生成集成：语法约束的Actor模块提议目标槽和掩码，独立的纯文本Verifier模块重构合格发现清单，当两者目标结构不一致时，Revision模块选择性重新执行共享Actor。EliSeg无需预定义目标标识、发现提示、点或边界框。在MIMIC-CXR-ILS数据集上的实验表明，EliSeg在各类发现上始终优于直接分割方法和“先提取后分割”级联模型，同时有效抑制报告中不合格提及的掩码；消融研究验证了验证和修订的互补作用，在CheXlocalize上的评估证明了EliSeg对外部数据集的有效迁移。代码可在https://github.com/...获取（注：原摘要中this http URL为占位符）。
英文摘要
Radiology reports describe clinical observations but do not specify executable segmentation targets. They may contain present, negated, prior,uncertain, or irrelevant findings, while multiple valid abnormalities may coexist. Existing segmentation methods largely bypass this ambiguity by receiving a target identity or spatial prompt before inference, which acts as a hidden target oracle. We study report-grounded abnormality segmentation, where a model must determine target eligibility, cardinality, and finding-to-mask correspondence directly from an unfiltered report before delineating the corresponding regions. We propose \textbf{EliSeg}, an atcor--verify--revise framework that integrates target construction with mask generation. A grammar-constrained Actor proposes target slots and masks, an independent text-only Verifier reconstructs the eligible finding inventory, and Revision selectively re-executes the shared Actor when their target structures disagree. EliSeg requires no predefined target identity, finding prompt, point, or bounding box. Experiments on MIMIC-CXR-ILS show that EliSeg consistently outperforms direct segmentation methods and extract-then-segment cascades across findings, while effectively suppressing masks for ineligible report mentions. Ablation studies confirm the complementary roles of verification and revision, and evaluation on CheXlocalize demonstrates effective transfer of the EliSeg to an external dataset.Code is available at https://github.com/Maybach-dream/EliSeg.
CommentsMinor revision: fixed author metadata rendering in the arXiv HTML version
