Figure 1.

The proposed overall framework of three‐stage deep neural network for AD diagnosis using MRI, PET, and SNP data. We first learn latent representations (i.e., high‐level features) for each modality independently in stage 1. Then, in stage 2, we learn joint latent feature representations for each pair of modality combination (e.g., MRI and PET, MRI and SNP, PET and SNP) by using the high‐level features learned from stage 1. Finally, in stage 3, we learn the diagnostic labels by fusing the learned joint latent feature representations from the stage 2 [Color figure can be viewed at http://wileyonlinelibrary.com]