Deep Grading based on Collective Artificial Intelligence for AD Diagnosis and Prognosis

Huy-Dung Nguyen (1) , Michaël Clément (1, 2) , Boris Mansencal (1) , Pierrick Coupé (1)

1 LaBRI - Laboratoire Bordelais de Recherche en Informatique
2 Bordeaux INP - Institut Polytechnique de Bordeaux

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.

Mots clés: Deep Grading; Collective Artificial Intelligence; Generalization; Alzheimer's disease classification; Mild Cognitive Impairment

Domaines: Informatique [cs]

Huy-Dung Nguyen, Michaël Clément, Boris Mansencal, Pierrick Coupé. Deep Grading based on Collective Artificial Intelligence for AD Diagnosis and Prognosis. Workshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI 2021, 2021, Strasbourg, France. ⟨10.1007/978-3-030-87444-5_3⟩. ⟨hal-03370898⟩