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

Autor: Pierrick Coupé, Huy-Dung Nguyen, Boris Mansencal, Michaël Clément
Přispěvatelé: Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Institut Polytechnique de Bordeaux (Bordeaux INP)
Jazyk: angličtina
Rok vydání: 2022
Předmět:
FOS: Computer and information sciences
Mild Cognitive Impairment
Computer science
Generalization
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
Convolutional neural network
Two stages
030218 nuclear medicine & medical imaging
Domain (software engineering)
03 medical and health sciences
0302 clinical medicine
Robustness (computer science)
FOS: Electrical engineering
electronic engineering
information engineering

[INFO]Computer Science [cs]
Deep Grading
Grading (education)
Interpretability
business.industry
Image and Video Processing (eess.IV)
Alzheimer's disease classification
Electrical Engineering and Systems Science - Image and Video Processing
Collective Artificial Intelligence
Graph (abstract data type)
Artificial intelligence
business
030217 neurology & neurosurgery
Zdroj: Interpretability of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data ISBN: 9783030874438
iMIMIC/TDA4MedicalData@MICCAI
Lecture Notes in Computer Science
Workshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI 2021
Workshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI 2021, 2021, Strasbourg, France
ISSN: 0302-9743
Popis: 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 admin note: substantial text overlap with arXiv:2206.03247
Databáze: OpenAIRE