  Alzheimer's disease and Frontotemporal dementia are common types of
neurodegenerative disorders that present overlapping clinical symptoms, making
their differential diagnosis very challenging. Numerous efforts have been done
for the diagnosis of each disease but the problem of multi-class differential
diagnosis has not been actively explored. In recent years, transformer-based
models have demonstrated remarkable success in various computer vision tasks.
However, their use in disease diagnostic is uncommon due to the limited amount
of 3D medical data given the large size of such models. In this paper, we
present a novel 3D transformer-based architecture using a deformable patch
location module to improve the differential diagnosis of Alzheimer's disease
and Frontotemporal dementia. Moreover, to overcome the problem of data
scarcity, we propose an efficient combination of various data augmentation
techniques, adapted for training transformer-based models on 3D structural
magnetic resonance imaging data. Finally, we propose to combine our
transformer-based model with a traditional machine learning model using brain
structure volumes to better exploit the available data. Our experiments
demonstrate the effectiveness of the proposed approach, showing competitive
results compared to state-of-the-art methods. Moreover, the deformable patch
locations can be visualized, revealing the most relevant brain regions used to
establish the diagnosis of each disease.
