Multicenter and Multichannel Pooling GCN for Early AD Diagnosis Based on Dual-Modality Fused Brain Network-Reference-Cited by-同舟云学术

Multicenter and Multichannel Pooling GCN for Early AD Diagnosis Based on Dual-Modality Fused Brain Network

Published:2023-02 Issue:2 Volume:42 Page:354-367
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Song Xuegang¹^ORCID,Zhou Feng²^ORCID,Frangi Alejandro F.³^ORCID,Cao Jiuwen⁴^ORCID,Xiao Xiaohua⁵,Lei Yi⁵,Wang Tianfu¹^ORCID,Lei Baiying¹^ORCID

Affiliation:

1. National- Regional Key TechnologyEngineering Laboratory for Medical Ultrasound and the Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, Shenzhen, China

2. Department of Industrial and Manufacturing, Systems Engineering, University of Michigan, Dearborn, MI, USA

3. Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, U.K.

4. Artificial Intelligence Institute, Hangzhou Dianzi University, Zhejiang, China

5. Health Science Center, First Affiliated Hospital, Shenzhen University, Shenzhen, China

Funder

Chinese National Science and Technology Pillar Program

National Natural Science Foundation of China

National Natural Science Foundation of Guangdong Province

Shenzhen Key Basic Research Project

Royal Academy of Engineering Chair in Emerging Technologies Scheme

Pengcheng Visiting Scholars Program from the Shenzhen Government

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

http://xplorestaging.ieee.org/ielx7/42/10035777/09810283.pdf?arnumber=9810283

Reference50 articles.

1. Graph convolution network with similarity awareness and adaptive calibration for disease-induced deterioration prediction

2. Self-calibrated brain network estimation and joint non-convex multi-task learning for identification of early Alzheimer's disease

3. Connectivity strength-weighted sparse group representation-based brain network construction for MCI classification

4. Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns

5. Deep Spatial-Temporal Feature Fusion From Adaptive Dynamic Functional Connectivity for MCI Identification

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