Multi-view imputation and cross-attention network based on incomplete longitudinal and multimodal data for conversion prediction of mild cognitive impairment-Reference-Cited by-同舟云学术

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Multi-view imputation and cross-attention network based on incomplete longitudinal and multimodal data for conversion prediction of mild cognitive impairment

Published:2023-11 Issue: Volume:231 Page:120761
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Wang Tao,Chen Xiumei,Zhang Xiaoling,Zhou Shuoling,Feng Qianjin,Huang Meiyan^ORCID

Funder

Alzheimer's Disease Neuroimaging Initiative

National Natural Science Foundation of China

Guangzhou Municipal Science and Technology Project

Basic and Applied Basic Research Foundation of Guangdong Province

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference57 articles.

1. Alzheimer’s disease diagnosis framework from incomplete multimodal data using convolutional neural networks;Abdelaziz;Journal of Biomedical Informatics,2021

2. Perspectives and challenges in patient stratification in Alzheimer’s disease;Abdelnour;Alzheimer’s Research & Therapy,2022

3. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease;Albert;Alzheimer’s & Dementia,2011

4. Tensor decompositions for learning latent variable models;Anandkumar;Journal of Machine Learning Research,2014

5. Data fusion based on searchlight analysis for the prediction of Alzheimer’s disease;Arco;Expert Systems with Applications,2021

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