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

TL;DR

This paper introduces a novel deep learning framework for Alzheimer's disease diagnosis and prognosis that combines a deep grading model with collective artificial intelligence and graph neural networks, improving robustness and interpretability.

Contribution

The paper presents a new two-stage deep framework utilizing collective AI and graph convolutional networks to enhance AD diagnosis and prognosis accuracy.

Findings

Achieved competitive performance on multiple datasets.

Enhanced robustness against domain shifts.

Outperformed several state-of-the-art methods.

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.