COOP-DHGNN: a Framework for Joint Classification and Prediction of Brain Functional Connectivity Using Sparse Trajectory Dataset with Application to Early Dementia-Reference-Cited by-同舟云学术

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COOP-DHGNN: a Framework for Joint Classification and Prediction of Brain Functional Connectivity Using Sparse Trajectory Dataset with Application to Early Dementia

Published:2022-12-17 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Big Data (Big Data)
language:
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Author:

Dong Zijian¹,Su Xian Chong Joanna¹,Cai Kok Bing¹,Helen Zhou Juan¹

Affiliation:

1. National Univerisity of Singapore,Singapore,Singapore

Funder

National Institutes of Health

National Institute of Biomedical Imaging and Bioengineering

BioClinica

Biogen

Bristol-Myers Squibb

Eisai

Elan

Eli Lilly and Company

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10020192/10020156/10021043.pdf?arnumber=10021043

Reference45 articles.

1. Deep EvoGraphNet Architecture for Time-Dependent Brain Graph Data Synthesis from a Single Timepoint

2. Brain graph synthesis by dual adversarial domain alignment and target graph prediction from a source graph

3. A unified framework for personalized regions selection and functional relation modeling for early MCI identification

4. Topology-guided cyclic brain connectivity generation using geometric deep learning

5. Federated Time-Dependent GNN Learning from Brain Connectivity Data with Missing Timepoints

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