  It is of great significance to apply deep learning for the early diagnosis of
Alzheimer's Disease (AD). In this work, a novel tensorizing GAN with high-order
pooling is proposed to assess Mild Cognitive Impairment (MCI) and AD. By
tensorizing a three-player cooperative game based framework, the proposed model
can benefit from the structural information of the brain. By incorporating the
high-order pooling scheme into the classifier, the proposed model can make full
use 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 work
to deal with classification on MRI images for AD diagnosis. Extensive
experimental results on Alzheimer's Disease Neuroimaging Initiative (ADNI)
dataset are reported to demonstrate that the proposed THS-GAN achieves superior
performance compared with existing methods, and to show that both tensor-train
and high-order pooling can enhance classification performance. The
visualization of generated samples also shows that the proposed model can
generate plausible samples for semi-supervised learning purpose.
