Synthesizing Missing Data using 3D Reversible GAN for Alzheimer's Disease

Multi-modal brain data has been extensively used for improving the accuracy of disease diagnosis by providing complementary information. A problem using multi-modality data is that the data is commonly incomplete for many subjects in the ADNI dataset. A straightforward strategy to tackle this challenge is to simply discard subjects with missing data, but this will greatly reduce the number of training subjects for learning reliable diagnostic models. In this work, we first adopted the RevGAN model to complete missing data. After that, a 3D convolutional neural network was designed to perform AD diagnosis by all subjects (with both real images and synthetic PET images). We tested our method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The results have demonstrated that our synthesized PET images with 3D-RevGAN are reasonable, and our method is successful in Alzheimer's diagnosis.