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It is of great significance to apply deep learning for the early diagnosis ofAlzheimer's Disease (AD). In this work, a novel tensorizing GAN with high-orderpooling is proposed to assess Mild Cognitive Impairment (MCI) and AD. Bytensorizing a three-player cooperative game based framework, the proposed modelcan benefit from the structural information of the brain. By incorporating thehigh-order pooling scheme into the classifier, the proposed model can make fulluse of the second-order statistics of the holistic Magnetic Resonance Imaging(MRI) images. To the best of our knowledge, the proposed Tensor-train,High-pooling and Semi-supervised learning based GAN (THS-GAN) is the first workto deal with classification on MRI images for AD diagnosis. Extensiveexperimental results on Alzheimer's Disease Neuroimaging Initiative (ADNI)dataset are reported to demonstrate that the proposed THS-GAN achieves superiorperformance compared with existing methods, and to show that both tensor-trainand high-order pooling can enhance classification performance. Thevisualization of generated samples also shows that the proposed model cangenerate plausible samples for semi-supervised learning purpose.

Tensorizing GAN with High-Order Pooling for Alzheimer's Disease Assessment