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This paper introduces ALTER, a novel framework for generating radiology reports from 3D CT scans by effectively modeling longitudinal changes through Anatomically Localized Temporal Evidence Representation. By integrating Global Prior Integration and Regional Proxy Differencing, ALTER captures patient-specific changes across multiple anatomical regions, allowing for a more coherent assessment of longitudinal states. The method achieves state-of-the-art performance on the RadGenome-ChestCT and CTRG-Chest-548K datasets, significantly improving the accuracy and completeness of automated report generation.
ALTER transforms how we generate CT reports by accurately modeling longitudinal changes across multiple anatomical regions, achieving state-of-the-art results in the process.
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.
