Contrastive Learning for Prediction of Alzheimer's Disease Using Brain 18F-FDG PET
Published 2022 View Full Article
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Title
Contrastive Learning for Prediction of Alzheimer's Disease Using Brain 18F-FDG PET
Authors
Keywords
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Journal
IEEE Journal of Biomedical and Health Informatics
Volume 27, Issue 4, Pages 1735-1746
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Online
2022-12-27
DOI
10.1109/jbhi.2022.3231905
References
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Note: Only part of the references are listed.- Anomaly Analysis of Alzheimer’s Disease in PET Images Using an Unsupervised Adversarial Deep Learning Model
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- European Association of Nuclear Medicine and European Academy of Neurology recommendations for the use of brain 18 F-fluorodeoxyglucose positron emission tomography in neurodegenerative cognitive impairment and dementia: Delphi consensus
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- Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer’s Disease Diagnosis
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- Classification of Alzheimer’s Disease by Combination of Convolutional and Recurrent Neural Networks Using FDG-PET Images
- (2018) Manhua Liu et al. Frontiers in Neuroinformatics
- A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain
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- (2015) Sergios Gatidis et al. NMR IN BIOMEDICINE
- Latent feature representation with stacked auto-encoder for AD/MCI diagnosis
- (2013) Heung-Il Suk et al. Brain Structure & Function
- Multimodal classification of Alzheimer's disease and mild cognitive impairment
- (2011) Daoqiang Zhang et al. NEUROIMAGE
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