Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/159700
Title: Pre-training 3D convolutional neural networks for prodromal alzheimer's disease classification
Authors: Jiang, Hongchao
Miao, Chunyan
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Jiang, H. & Miao, C. (2022). Pre-training 3D convolutional neural networks for prodromal alzheimer's disease classification. 2022 International Joint Conference on Neural Networks (IJCNN). https://dx.doi.org/10.1109/IJCNN55064.2022.9891966
Project: NRF-NRFI05-2019-0002 
MOH/NIC/HAIG03/2017 
Abstract: Alzheimer's disease (AD) is a chronic neurodegenerative disease that causes cognitive deficits, which severely interfere with daily life. Convolutional Neural Networks (CNNs) have been used to analyze Medical Resonance Imaging (MRI) scans for the early detection of AD. Prior works have explored supervised pre-training, unsupervised pre-training, and joint training to improve the diagnostic accuracy of CNNs. However, there is no consensus on the best approach. We compare the different pre-training methods in a standardized setting. Our experiments find that supervised pre-training and joint training outperform unsupervised pre-training when data is extremely limited. With more data, unsupervised pre-training closes the performance gap and, in some cases, outperforms supervised pre-training and joint training. In addition, we propose a simple hybrid approach of unsupervised pre-training followed by joint training that achieves the best performance.
URI: https://hdl.handle.net/10356/159700
ISBN: 978-1-7281-8671-9
ISSN: 2161-4407
DOI: 10.1109/IJCNN55064.2022.9891966
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:IGS Conference Papers
SCSE Conference Papers

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