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

Abstract

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.

arXiv commentarXiv admin note: substantial text overlap with arXiv:2206.03247

References in corpus (6)

• Semi-Supervised Classification with Graph Convolutional Networks
• mixup: Beyond Empirical Risk Minimization
• Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation
• Alzheimer's Disease Diagnostics by Adaptation of 3D Convolutional Network
• An Explainable 3D Residual Self-Attention Deep Neural Network FOR Joint Atrophy Localization and Alzheimer's Disease Diagnosis using Structural MRI
• Graph of brain structures grading for early detection of Alzheimer's disease