A deep generative multimodal imaging genomics framework for Alzheimer's disease prediction-Reference-Cited by-同舟云学术

A deep generative multimodal imaging genomics framework for Alzheimer's disease prediction

Published:2022-11 Issue: Volume: Page:
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Container-title:2022 IEEE 22nd International Conference on Bioinformatics and Bioengineering (BIBE)
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Author:

Dolci Giorgio¹,Rahaman Md Abdur¹,Chen Jiayu¹,Duan Kuaikuai¹,Fu Zening¹,Abrol Anees¹,Menegaz Gloria²,Calhoun Vince D.¹

Affiliation:

1. TReNDS, Georgia State, Georgia Tech, Emory,Atlanta,GA,USA

2. University of Verona,Dept. of Computer Science,Verona,Italy

Funder

National Science Foundation (NSF)

National Institutes of Health (NIH)

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9973398/9973401/09973645.pdf?arnumber=9973645

Reference34 articles.

1. Multimodal classification of Alzheimer's disease and mild cognitive impairment

2. Classification of Alzheimer's disease by combination of convolutional and recurrent neural networks using FDG-PET images;liu;Frontiers in Neuroinformatics,2018

3. Pairwise feature-based generative adversarial network for incomplete multi-modal Alzheimer's disease diagnosis;ye;The Visual Computer,2022

4. On Improved 3D-CNN-Based Binary and Multiclass Classification of Alzheimer’s Disease Using Neuroimaging Modalities and Data Augmentation Methods

5. MRI to PET Cross-Modality Translation using Globally and Locally Aware GAN (GLA-GAN) for Multi-Modal Diagnosis of Alzheimer's Disease;sikka;ArXiv Preprint,2021

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