TY - JOUR
T1 - Incomplete multi-modal representation learning for Alzheimer's disease diagnosis
AU - Liu, Yanbei
AU - Fan, Lianxi
AU - Zhang, Changqing
AU - Zhou, Tao
AU - Xiao, Zhitao
AU - Geng, Lei
AU - Shen, Dinggang
N1 - Funding Information:
This work is supported in part by the National Natural Science Foundation of China (No. 61901297 ), Tianjin Science and Technology Major Projects and Engineering (No. 17ZXSCSY00060, 17ZXSCSY00090), Natural Science Foundation of Tianjin of China (19JCYBJC15200), Program for Innovative Research Team in University of Tianjin (No. TD13-5034).
Funding Information:
This work is supported in part by the National Natural Science Foundation of China (No. 61901297), Tianjin Science and Technology Major Projects and Engineering (No. 17ZXSCSY00060, 17ZXSCSY00090), Natural Science Foundation of Tianjin of China (19JCYBJC15200), Program for Innovative Research Team in University of Tianjin (No. TD13-5034).
Publisher Copyright:
© 2020
PY - 2021/4
Y1 - 2021/4
N2 - Alzheimers disease (AD) is a complex neurodegenerative disease. Its early diagnosis and treatment have been a major concern of researchers. Currently, the multi-modality data representation learning of this disease is gradually becoming an emerging research field, attracting widespread attention. However, in practice, data from multiple modalities are only partially available, and most of the existing multi-modal learning algorithms can not deal with the incomplete multi-modality data. In this paper, we propose an Auto-Encoder based Multi-View missing data Completion framework (AEMVC) to learn common representations for AD diagnosis. Specifically, we firstly map the original complete view to a latent space using an auto-encoder network framework. Then, the latent representations measuring statistical dependence learned from the complete view are used to complement the kernel matrix of the incomplete view in the kernel space. Meanwhile, the structural information of original data and the inherent association between views are maintained by graph regularization and Hilbert-Schmidt Independence Criterion (HSIC) constraints. Finally, a kernel based multi-view method is applied to the learned kernel matrix for the acquisition of common representations. Experimental results achieved on Alzheimers Disease Neuroimaging Initiative (ADNI) datasets validate the effectiveness of the proposed method.
AB - Alzheimers disease (AD) is a complex neurodegenerative disease. Its early diagnosis and treatment have been a major concern of researchers. Currently, the multi-modality data representation learning of this disease is gradually becoming an emerging research field, attracting widespread attention. However, in practice, data from multiple modalities are only partially available, and most of the existing multi-modal learning algorithms can not deal with the incomplete multi-modality data. In this paper, we propose an Auto-Encoder based Multi-View missing data Completion framework (AEMVC) to learn common representations for AD diagnosis. Specifically, we firstly map the original complete view to a latent space using an auto-encoder network framework. Then, the latent representations measuring statistical dependence learned from the complete view are used to complement the kernel matrix of the incomplete view in the kernel space. Meanwhile, the structural information of original data and the inherent association between views are maintained by graph regularization and Hilbert-Schmidt Independence Criterion (HSIC) constraints. Finally, a kernel based multi-view method is applied to the learned kernel matrix for the acquisition of common representations. Experimental results achieved on Alzheimers Disease Neuroimaging Initiative (ADNI) datasets validate the effectiveness of the proposed method.
KW - Alzheimers disease diagnosis
KW - auto-encoder network
KW - incomplete multi-modality data
KW - kernel completion
KW - multi-modal representation learning
UR - http://www.scopus.com/inward/record.url?scp=85099397565&partnerID=8YFLogxK
U2 - 10.1016/j.media.2020.101953
DO - 10.1016/j.media.2020.101953
M3 - Article
C2 - 33460880
AN - SCOPUS:85099397565
VL - 69
JO - Medical Image Analysis
JF - Medical Image Analysis
SN - 1361-8415
M1 - 101953
ER -