Cost-Sensitive Weighted Contrastive Learning Based on Graph Convolutional Networks for Imbalanced Alzheimer’s Disease Staging-Reference-Cited by-同舟云学术

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Cost-Sensitive Weighted Contrastive Learning Based on Graph Convolutional Networks for Imbalanced Alzheimer’s Disease Staging

Published:2024-09 Issue:9 Volume:43 Page:3126-3136
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Hu Yan¹^ORCID,Wang Jun¹^ORCID,Zhu Hao¹,Li Juncheng¹^ORCID,Shi Jun¹^ORCID

Affiliation:

1. Key Laboratory of Specialty Fiber Optics and Optical Access Networks, the Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute for Advanced Communication and Data Science, and the School of Communication and Information Engineering, Shanghai University, Shanghai, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/42/10663877/10500867.pdf?arnumber=10500867

Reference50 articles.

1. The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods

2. Metabolic and vascular origins of the BOLD effect: Implications for imaging pathology and resting-state brain function

3. Topographical Information-Based High-Order Functional Connectivity and Its Application in Abnormality Detection for Mild Cognitive Impairment

4. Grab‐AD : Generalizability and reproducibility of altered brain activity and diagnostic classification in Alzheimer's Disease

5. Functional connectivity between white matter and gray matter based on fMRI for Alzheimer's disease classification

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