Deep Grading based on Collective Artificial Intelligence for AD Diagnosis and Prognosis
Autor: | Pierrick Coupé, Huy-Dung Nguyen, Boris Mansencal, Michaël Clément |
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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 |
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