  Alzheimer's Disease is the most common cause of dementia. Accurate diagnosis
and prognosis of this disease are essential to design an appropriate treatment
plan, increasing the life expectancy of the patient. Intense research has been
conducted on the use of machine learning to identify Alzheimer's Disease from
neuroimaging data, such as structural magnetic resonance imaging. In recent
years, advances of deep learning in computer vision suggest a new research
direction for this problem. Current deep learning-based approaches in this
field, however, have a number of drawbacks, including the interpretability of
model decisions, a lack of generalizability information and a lower performance
compared to traditional machine learning techniques. In this paper, we design a
two-stage framework to overcome these limitations. In the first stage, an
ensemble of 125 U-Nets is used to grade the input image, producing a 3D map
that reflects the disease severity at voxel-level. This map can help to
localize abnormal brain areas caused by the disease. In the second stage, we
model a graph per individual using the generated grading map and other
information about the subject. We propose to use a graph convolutional neural
network classifier for the final classification. As a result, our framework
demonstrates comparative performance to the state-of-the-art methods in
different datasets for both diagnosis and prognosis. We also demonstrate that
the use of a large ensemble of U-Nets offers a better generalization capacity
for our framework.
