DA-S2RL: Disease-aware shared-specific representation learning for heterogeneous image-EHR Alzheimer's disease diagnosis.

Combining electronic health records (EHR) with medical images is important for improving the diagnosis of Alzheimer's disease (AD). However, neuroimages and EHR differ greatly in semantic content and data distribution. Neuroimages mainly reflect brain structure and metabolic abnormalities, while EHR records clinical symptoms and behavioral changes. Directly fusing these heterogeneous modalities may mix shared disease information with modality-specific diagnostic cues, resulting in redundant and ambiguous representations. To address this problem, we propose DA-S2RL, a disease-aware shared-specific multimodal representation learning framework for AD diagnosis. DA-S2RL organizes heterogeneous image and EHR features into disease-related shared semantics and modality-specific diagnostic residuals. The core of DA-S2RL is the shared-specific cross-modal coding (SCC) module. It strengthens cross-modal semantic consistency, suppresses redundant correlations, and enables mutual refinement between shared semantics and diagnostic residuals. Before SCC, image features are integrated into a compact image representation, and EHR features are encoded into the same feature space. Experiments on ADNI and NACC demonstrate that explicitly organizing heterogeneous image-EHR representations into shared disease semantics and modality-specific residuals improves multimodal diagnostic learning under different image-EHR settings.