CMAP-Fusion: A cross-modal feature selection and model pruning framework for laboratory and imaging data

Cross-modal fusion of medical imaging and laboratory data is a key pathway for accurate diagnosis of diseases, yet it is constrained by issues such as the modal heterogeneity gap, accumulation of feature redundancy, and efficiency imbalance. Existing methods struggle to balance precision and clinical adaptability, and some rely on simulated data leading to limited generalization ability. To address these challenges, we propose the Cross-Modal Alignment-Pruning Fusion model (CMAP-Fusion), which a