Abstract: Alzheimer’s disease(AD) is a neurodegenerative disorder with an extremely complex pathogenesis and is nearly incurable.Due to its irreversible progression,early diagnosis is crucial for patients.Studies show that the joint analysis of multimodal medical imaging data can help reveal the pathological characteristics of AD at different stages and provide a more comprehensive perspective,thereby facilitating early diagnosis and timely intervention.However,existing multimodal methods often fuse information from different modalities directly,neglecting the underlying shared and modality-specific features,which limits the model’s ability to deeply extract AD-related representations.To address this issue,this paper proposes DHMoE,a multimodal deep lear-ning model that integrates feature decoupling learning and a heterogeneous mixture-of-experts(MoE) network,aiming for accurate AD diagnosis.This method constructs modality-specific deep encoding networks to decouple the features of each modality into shared and modality-specific components,which are then mapped into public and private subspaces,respectively.This enables deep modeling of both commonalities and differences within multimodal data,enhancing the model’s representation and discriminative capabilities.Furthermore,a heterogeneous MoE structure is introduced,where the decoupled features are fed into corresponding expert networks.Through a dynamic weight control mechanism,the model achieves adaptive fusion and discriminative modeling of multi-granularity features.Finally,experiments on the ADNI dataset demonstrate that the proposed method outperforms several mainstream approaches in multiple evaluation metrics such as accuracy(ACC) and area under the curve(AUC),achieving more accurate classification results.

Key words: Alzheimer’s disease, sMRI, PET, Multimodal, Feature decoupling, Mixture of experts model