  The diagnosis of early stages of Alzheimer's disease (AD) is essential for
timely treatment to slow further deterioration. Visualizing the morphological
features for the early stages of AD is of great clinical value. In this work, a
novel Multidirectional Perception Generative Adversarial Network (MP-GAN) is
proposed to visualize the morphological features indicating the severity of AD
for patients of different stages. Specifically, by introducing a novel
multidirectional mapping mechanism into the model, the proposed MP-GAN can
capture the salient global features efficiently. Thus, by utilizing the
class-discriminative map from the generator, the proposed model can clearly
delineate the subtle lesions via MR image transformations between the source
domain and the pre-defined target domain. Besides, by integrating the
adversarial loss, classification loss, cycle consistency loss and \emph{L}1
penalty, a single generator in MP-GAN can learn the class-discriminative maps
for multiple-classes. Extensive experimental results on Alzheimer's Disease
Neuroimaging Initiative (ADNI) dataset demonstrate that MP-GAN achieves
superior performance compared with the existing methods. The lesions visualized
by MP-GAN are also consistent with what clinicians observe.
