Pre-training 3D convolutional neural networks for prodromal alzheimer's disease classification
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...
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| Main Authors: | , |
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| Other Authors: | |
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/159700 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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. |
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