  Alzheimer's disease and Frontotemporal dementia are two major types of
dementia. Their accurate diagnosis and differentiation is crucial for
determining specific intervention and treatment. However, differential
diagnosis of these two types of dementia remains difficult at the early stage
of disease due to similar patterns of clinical symptoms. Therefore, the
automatic classification of multiple types of dementia has an important
clinical value. So far, this challenge has not been actively explored. Recent
development of deep learning in the field of medical image has demonstrated
high performance for various classification tasks. In this paper, we propose to
take advantage of two types of biomarkers: structure grading and structure
atrophy. To this end, we propose first to train a large ensemble of 3D U-Nets
to locally discriminate healthy versus dementia anatomical patterns. The result
of these models is an interpretable 3D grading map capable of indicating
abnormal brain regions. This map can also be exploited in various
classification tasks using graph convolutional neural network. Finally, we
propose to combine deep grading and atrophy-based classifications to improve
dementia type discrimination. The proposed framework showed competitive
performance compared to state-of-the-art methods for different tasks of disease
detection and differential diagnosis.
