  Accurate diagnosis and prognosis of Alzheimer's disease are crucial to
develop new therapies and reduce the associated costs. Recently, with the
advances of convolutional neural networks, methods have been proposed to
automate these two tasks using structural MRI. However, these methods often
suffer from lack of interpretability, generalization, and can be limited in
terms of performance. In this paper, we propose a novel deep framework designed
to overcome these limitations. Our framework consists of two stages. In the
first stage, we propose a deep grading model to extract meaningful features. To
enhance the robustness of these features against domain shift, we introduce an
innovative collective artificial intelligence strategy for training and
evaluating steps. In the second stage, we use a graph convolutional neural
network to better capture AD signatures. Our experiments based on 2074 subjects
show the competitive performance of our deep framework compared to
state-of-the-art methods on different datasets for both AD diagnosis and
prognosis.
