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ALTER: Modeling Longitudinal Changes via Regional Differencing for 3D CT Report Generation

arXiv 2608.05615 ↗First posted 6 Aug 2026cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 42 references that are in the map. First posted on arXiv 6 Aug 2026.

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25 of 42 references.

References · 25

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2014CIDEr: Consensus-based Image Description Evaluationcited by 2,027
  3. 2022TotalSegmentator: robust segmentation of 104 anatomical structures in CT imagescited by 597
  4. 2020Generating Radiology Reports via Memory-driven Transformercited by 363
  5. 2021RadGraph: Extracting Clinical Entities and Relations from Radiology Reportscited by 310
  6. 2017On the Automatic Generation of Medical Imaging Reportscited by 215
  7. 2023Learning to Exploit Temporal Structure for Biomedical Vision-Language Processingcited by 210
  8. 2024Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomographycited by 192
  9. 2022Cross-modal Memory Networks for Radiology Report Generationcited by 183
  10. 2021Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generationcited by 160
  11. 2020When Radiology Report Generation Meets Knowledge Graphcited by 144
  12. 2018TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rayscited by 139
13 more
  1. 2024GREEN: Generative Radiology Report Evaluation and Error Notationcited by 124
  2. 2023R2GenGPT: Radiology Report Generation with Frozen LLMscited by 111
  3. 2019Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generationcited by 101
  4. 2024CT2Rep: Automated Radiology Report Generation for 3D Medical Imagingcited by 96
  5. 2022AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Generationcited by 65
  6. 2023Utilizing Longitudinal Chest X-Rays and Reports to Pre-Fill Radiology Reportscited by 34
  7. 2024HERGen: Elevating Radiology Report Generation with Longitudinal Datacited by 32
  8. 2024Large Language Model with Region-guided Referring and Grounding for CT Report Generationcited by 27
  9. 2023Controllable Chest X-Ray Report Generation from Longitudinal Representationscited by 23
  10. 2023RECAP: Towards Precise Radiology Report Generation via Dynamic Disease Progression Reasoningcited by 22
  11. 2025CoCa-CXR: Contrastive Captioners Learn Strong Temporal Structures for Chest X-Ray Vision-Language Understandingcited by 11
  12. 2025PriorRG: Prior-Guided Contrastive Pre-training and Coarse-to-Fine Decoding for Chest X-ray Report Generationcited by 9
  13. 2026DiffVP: Differential Visual Semantic Prompting for LLM-Based CT Report Generationcited by 1

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