  Frontotemporal dementia and Alzheimer's disease are two common forms of
dementia and are easily misdiagnosed as each other due to their similar pattern
of clinical symptoms. Differentiating between the two dementia types is crucial
for determining disease-specific intervention and treatment. Recent development
of Deep-learning-based approaches in the field of medical image computing are
delivering some of the best performance for many binary classification tasks,
although its application in differential diagnosis, such as neuroimage-based
differentiation for multiple types of dementia, has not been explored. In this
study, a novel framework was proposed by using the Generative Adversarial
Network technique to distinguish FTD, AD and normal control subjects, using
volumetric features extracted at coarse-to-fine structural scales from Magnetic
Resonance Imaging scans. Experiments of 10-folds cross-validation on 1,954
images achieved high accuracy. With the proposed framework, we have
demonstrated that the combination of multi-scale structural features and
synthetic data augmentation based on generative adversarial network can improve
the performance of challenging tasks such as differentiating Dementia
sub-types.
