Modeling Continuous Spatio-Temporal Disease Evolution for Longitudinal Radiology Report Generation

Radiology report generation aims to assist radiologists in drafting medical reports by automatically generating descriptive text from images, thereby reducing workload and improving diagnostic efficiency and accuracy. Existing report generation methods often treat disease changes under irregular temporal sampling as fixed-interval discrete jumps, resulting in inaccurate disease state characterization. To address it, we propose a novel **Co**ntinuous spatio-temporal **D**isease **E**volution framework for longitudinal **R**adiology **R**eport **G**eneration (CoDE-RRG), which models continuous disease evolution from arbitrary numbers of prior visits and multi-view images. Specifically, we propose a view-stable multi-view fusion module, where a primary view serves as an anchor to aggregate information from all auxiliary views. Departing from traditional discrete modeling of longitudinal changes, we first employ disease-guided dimensionality reduction to project high-dimensional visual features into a low-dimensional disease-specific state space, and then innovatively introduce Neural Ordinary Differential Equations to compute the continuous temporal evolution trajectories of these disease states. It allows for the construction of individualized disease progression tracks that align with actual physiological changes. Extensive experiments demonstrate that our method consistently improves the modeling of longitudinal disease evolution and current disease states, with relative gains of up to 2.3% in BLEU-4 and 4.6% in CheXbert F1 over state-of-the-art methods. Our code is available at https://anonymous.4open.science/r/CoDE-RRG-E007/.