Deep Self-Reconstruction Fusion Similarity Hashing for the Diagnosis of Alzheimer's Disease on Multi-Modal Data-Reference-Cited by-同舟云学术

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Deep Self-Reconstruction Fusion Similarity Hashing for the Diagnosis of Alzheimer's Disease on Multi-Modal Data

Published:2024-06 Issue:6 Volume:28 Page:3513-3522
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Wu Tian-Ru¹^ORCID,Jiao Cui-Na¹^ORCID,Cui Xinchun²^ORCID,Wang Yan-Li³^ORCID,Zheng Chun-Hou¹^ORCID,Liu Jin-Xing⁴^ORCID

Affiliation:

1. School of Computer Science, Qufu Normal University, Rizhao, Shandong, China

2. School of Foundational Education, University of Health and Rehabilitation Sciences, Qingdao, China

3. Qingdao Central Hospital, University of Health and Rehabilitation Sciences, Qingdao, China

4. School of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6221020/10550931/10490011.pdf?arnumber=10490011

Reference41 articles.

1. 2023 Alzheimer's disease facts and figures

2. Forecasting the global burden of Alzheimer's disease

3. Enhancer variants associated with Alzheimer’s disease affect gene expression via chromatin looping

4. Associating Multi-Modal Brain Imaging Phenotypes and Genetic Risk Factors via a Dirty Multi-Task Learning Method

5. Identify connectome between genotypes and brain network phenotypes via deep self-reconstruction sparse canonical correlation analysis

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1. Alzheimer’s disease diagnosis from single and multimodal data using machine and deep learning models: Achievements and future directions;Expert Systems with Applications;2024-12

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