Visit-order Exchange Equivariance Modeling for Region-Level Longitudinal Report Generation

Automated radiology report generation (RRG) has proven its potential to reduce the workload of radiologists. However, most existing RRG methods rely on single-visit data, overlooking the longitudinal changes in the disease. While existing longitudinal methods track disease progression, they typically rely on global features that limit fine-grained regional correspondence, and they are prone to temporal template shortcuts due to visit-order sensitivity. To address this, we propose VEE-RG, a longitudinal radiology report generation framework that generates visit-order stable reports. Specifically, to overcome spatial misalignments and missed detections caused by patient posture variations, we design a paired-region stabilization module to construct reliable visual correspondence. Furthermore, we propose an exchange-equivariant change factorization module that decouples longitudinal differences into symmetric and antisymmetric components, effectively mitigating order sensitivity and temporal shortcuts. Finally, anonymized region-level contrastive learning is introduced to align modalities, preventing shortcut matching via anatomical names and enforcing authentic pathology learning. Extensive experiments on two large public datasets demonstrate that VEE-RG exhibits more stable temporal modeling capabilities and outperforms competitive methods, achieving a 1.7% improvement in CheXbert F1-score on the MIMIC-CXR dataset and a 4.1% improvement in F1-score on the Chest ImaGenome dataset. Our code is available at https://anonymous.4open.science/r/VEE-RG-C1F4.