TY - GEN
T1 - Modeling Disease Progression Flexibly with Nonlinear Disease Structure via Multi-task Learning
AU - Zhou, Menghui
AU - Wang, Xulong
AU - Yang, Yun
AU - Nan, Fengtao
AU - Zhang, Yu
AU - Qi, Jun
AU - Yang, Po
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Alzheimer's Disease (AD) is the most common dementia characterized by loss of brain function. Multi-tasking learning methods have been widely used to predict cognitive performance and select important imaging biomarkers in AD research. The temporal smoothness assumption, prevalent for modeling AD progression, means the difference between cognitive scores at two consecutive time points is relatively small. However, it's not appropriate due to the presence of sample disturbance and the effectiveness of drug therapy. In addition, many multi-task learning methods select discriminative feature subset from MRI features, assuming that correlations between tasks are consistent, which ignores the complex intrinsic correlation structure of tasks. In this paper, we present a multi-task learning framework which utilizes generalized fused Lasso and generalized group Lasso (GFGGL for abbreviation) to model the disease progression with the complex intrinsic nonlinear structures of disease. The proposed framework is more flexible to utilize the inherent nonlinear relation of AD than existing methods for the reason of we represent the intrinsic structure as three correlation matrices which are functions of super parameters. The framework involves (1) two nonlinear structures of disease progression and (2) one nonlinear structure among tasks. An efficient optimization method is designed for the difficult optimization problem due to the presence of three nonsmooth penalties. Extensive experimental results using dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate the effectiveness of the proposed method.
AB - Alzheimer's Disease (AD) is the most common dementia characterized by loss of brain function. Multi-tasking learning methods have been widely used to predict cognitive performance and select important imaging biomarkers in AD research. The temporal smoothness assumption, prevalent for modeling AD progression, means the difference between cognitive scores at two consecutive time points is relatively small. However, it's not appropriate due to the presence of sample disturbance and the effectiveness of drug therapy. In addition, many multi-task learning methods select discriminative feature subset from MRI features, assuming that correlations between tasks are consistent, which ignores the complex intrinsic correlation structure of tasks. In this paper, we present a multi-task learning framework which utilizes generalized fused Lasso and generalized group Lasso (GFGGL for abbreviation) to model the disease progression with the complex intrinsic nonlinear structures of disease. The proposed framework is more flexible to utilize the inherent nonlinear relation of AD than existing methods for the reason of we represent the intrinsic structure as three correlation matrices which are functions of super parameters. The framework involves (1) two nonlinear structures of disease progression and (2) one nonlinear structure among tasks. An efficient optimization method is designed for the difficult optimization problem due to the presence of three nonsmooth penalties. Extensive experimental results using dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate the effectiveness of the proposed method.
KW - Alzheimer's Disease
KW - generalized fused Lasso
KW - generalized group Lasso
KW - multi-task learning
KW - nonlinear structure
UR - http://www.scopus.com/inward/record.url?scp=85128778875&partnerID=8YFLogxK
U2 - 10.1109/MSN53354.2021.00063
DO - 10.1109/MSN53354.2021.00063
M3 - Conference Proceeding
AN - SCOPUS:85128778875
T3 - Proceedings - 2021 17th International Conference on Mobility, Sensing and Networking, MSN 2021
SP - 366
EP - 373
BT - Proceedings - 2021 17th International Conference on Mobility, Sensing and Networking, MSN 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th International Conference on Mobility, Sensing and Networking, MSN 2021
Y2 - 13 December 2021 through 15 December 2021
ER -