通过区域差分建模3D CT纵向变化,提升报告生成准确性
ALTER: Modeling Longitudinal Changes via Regional Differencing for 3D CT Report Generation

- 用历史影像构建全局先验,定位当前病变区域变化
- 区域代理差分提取局部时间差异,融合多区域变化信息
- 生成带变化感知的软提示,适合临床随访报告场景
计算机断层扫描(CT)广泛用于临床诊断与纵向随访,但基于三维CT自动生成准确完整放射科报告仍具挑战。现有方法虽通过建模解剖区域提升图像与文本细粒度对齐,但主要聚焦当前检查,未能充分刻画个体区域内的患者特异性纵向变化。同时,时间间隔变化常分布于多个解剖区域,导致整体纵向状态评估困难。本文提出解剖局部化时间证据表示(ALTER),通过全局先验整合(GPI)引入既往影像与报告建立历史上下文;区域代理差分(RPD)使每个当前解剖区域从单一共享编码的既往体积中检索历史代理,获取局部时间证据;区间变化融合(ICF)将当前异常状态与区域分布差异联合表示,转化为变化感知的软提示以指导报告生成。ALTER在RadGenome-ChestCT验证集和CTRG-Chest-548K测试集上多数评估指标达到最优。代码与数据预处理细节见https://github.com/peytonkarlie/ALTER/tree/main。
原文摘要 · Abstract (English)
Computed tomography (CT) is widely used for clinical diagnosis and longitudinal follow-up, yet automatically generating accurate and complete radiology reports from three-dimensional (3D) CT remains challenging. Existing methods improve fine-grained correspondence between images and text by modeling anatomical regions, but remain centered on the current examination. Consequently, patient-specific longitudinal changes within individual regions remain insufficiently modeled. Meanwhile, interval changes are often distributed across multiple anatomical regions, complicating a coherent assessment of the overall longitudinal state. We propose Anatomically Localized Temporal Evidence Representation (ALTER) to address these limitations. Global Prior Integration (GPI) incorporates the prior CT and report to establish historical context for the current examination. Regional Proxy Differencing (RPD) enables each current anatomical region to retrieve a historical proxy from a single shared encoding of the prior volume and to derive localized interval evidence. Interval Change Fusion (ICF) further combines current abnormality states with region-distributed differences, converting their joint representation into change-aware soft prompts that guide report generation. ALTER achieves state-of-the-art results on most evaluation metrics across the RadGenome-ChestCT validation and CTRG-Chest-548K test sets. Code and data preprocessing details are available at https://github.com/peytonkarlie/ALTER/tree/main.
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