TIM：面向纵向放射学报告生成的时序解耦与迭代互精炼模型
TIM: Temporal Decoupling with Iterative Mutual-Refinement Model for Longitudinal Radiology Report Generation
把「空间病理特征」与「时序演变模式」显式解耦到不同表征空间，再用前后两次检查的报告互相纠错迭代精炼——这套解耦+互反馈的思路可迁移到任何需要跨时间点比较的视觉-语言生成任务（如变化描述、时序 VQA）。
原文依据 explicitly decouples spatial pathology from temporal progression and iteratively refines reports through mutual feedback
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论自动放射学报告生成（RRG）旨在将医学图像转化为诊断文本，从而减轻放射科医师的工作负担并规范临床文档。然而，现有方法主要聚焦于单时间点分析，无法捕捉跨纵向检查的疾病演变。尽管近期的纵向 RRG（LRRG）方法引入了历史数据，但它们往往将不同时间点的图像混合在单一表征空间中，导致语义模糊与时序推理不一致。本文提出时序解耦与迭代互精炼模型（TIM），这是一个两阶段框架，显式地将空间病理与时序进展解耦，并通过相互反馈迭代精炼报告。第一阶段执行时序解耦表征学习，将时序演变模式与疾病特异特征分离，并为先前检查与当前检查分别生成放射学报告。第二阶段引入互报告精炼机制，识别先前报告中的诊断不一致之处，并通过误差敏感的反馈迭代校正先前报告与当前报告。在 Longitudinal-MIMIC 数据集上的实验表明，TIM 优于现有的单图像与纵向基线，在语言指标与临床指标上均达到新的最优性能。代码见 https://github.com/yihengd/TIM。
英文原文摘要 1482 字符
Automatic radiology report generation (RRG) aims to translate medical images into diagnostic text, reducing radiologists' workload and standardizing clinical documentation. Nonetheless, existing approaches mainly focus on single-time point analysis and fail to capture temporal disease evolution across longitudinal examinations. While recent longitudinal RRG (LRRG) approaches incorporate historical data, they often combine images from different time points within a single representation space, leading to blurred semantics and inconsistent temporal reasoning. In this work, we propose a Temporal Decoupling with Iterative MutualRefinement Model (TIM), a two-stage framework that explicitly decouples spatial pathology from temporal progression and iteratively refines reports through mutual feedback. Stage I performs temporal-decoupled representation learning, separating temporal evolution patterns from disease-specific features and generating radiology reports for both prior and current studies. Stage II introduces a mutual report refinement mechanism that identifies diagnostic inconsistencies within prior reports and iteratively rectifies both prior and current reports through error-sensitive feedback. Experiments on the Longitudinal-MIMIC dataset demonstrate that TIM surpasses existing single-image and longitudinal baselines, achieving new state-of-the-art performance across both language and clinical metrics. Code is available at https://github.com/yihengd/TIM.