AI 中文总结
针对3D CT报告生成中纵向变化建模不足的问题，提出ALTER模型，通过GPI、RPD、ICF模块实现，在两个数据集上取得最优性能。
AI 中文摘要
计算机断层扫描（CT）广泛应用于临床诊断与纵向随访，但从三维（3D）CT自动生成准确完整的放射学报告仍具挑战性。现有方法通过建模解剖区域提升图像与文本的细粒度对应关系，但仍以当前检查为中心，导致个体区域内患者特异性纵向变化建模不足；同时，间隔变化常分布于多个解剖区域，难以对整体纵向状态进行连贯评估。本文提出解剖定位时间证据表示（Anatomically Localized Temporal Evidence Representation，ALTER）以解决上述局限：全局先验整合（Global Prior Integration，GPI）整合先验CT与报告，为当前检查建立历史上下文；区域代理差分（Regional Proxy Differencing，RPD）使每个当前解剖区域能从先验体积的单个共享编码中检索历史代理，进而生成本地化间隔证据；间隔变化融合（Interval Change Fusion，ICF）进一步将当前异常状态与区域分布差异结合，将其联合表示转换为感知变化的软提示，指导报告生成。ALTER在RadGenome-ChestCT验证集与CTRG-Chest-548K测试集的多数评估指标上达到了当前最优性能。代码与数据预处理详情可访问该https URL。
英文摘要
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.
