TY - GEN
T1 - Continually Modeling Alzheimer’s Disease Progression via Deep Multi-order Preserving Weight Consolidation
AU - Zhang, Jie
AU - Wang, Yalin
N1 - Funding Information:
Y. Wang—The research was supported in part by NIH (RF1AG051710, R01EB025032 and U54EB020403). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research.
PY - 2019
Y1 - 2019
N2 - Alzheimer’s disease (AD) is the most common type of dementia. Identifying biomarkers that can track AD at early stages is crucial for therapy to be successful. Many researchers have developed models to predict cognitive impairments by employing valuable longitudinal imaging information along the progression of the disease. However, previous methods model the problem either in the isolated single-task mode or multi-task batch mode, which ignores the fact that the longitudinal data always arrive in a continuous time sequence and, in reality, there are rich types of longitudinal data to apply our learned model to. To this end, we continually model the AD progression in time sequence via a proposed novel Deep Multi-order Preserving Weight Consolidation (DMoPWC) to simultaneously (1) discover the inter and inner relations among different cognitive measures at different time points and utilize such relations to enhance the learning of associations between imaging features and clinical scores; (2) continually learn new longitudinal patients’ images to overcome forgetting the previously learned knowledge without access to the old data. Moreover, inspired by recent breakthroughs of Recurrent Neural Network, we consider time-order knowledge to further reinforce the statistical power of DMoPWC and ensure features at a particular time will be temporally ahead of the features at its subsequential times. Empirical studies on the longitudinal brain image dataset demonstrate that DMoPWC achieves superior performance over other AD prognosis algorithms.
AB - Alzheimer’s disease (AD) is the most common type of dementia. Identifying biomarkers that can track AD at early stages is crucial for therapy to be successful. Many researchers have developed models to predict cognitive impairments by employing valuable longitudinal imaging information along the progression of the disease. However, previous methods model the problem either in the isolated single-task mode or multi-task batch mode, which ignores the fact that the longitudinal data always arrive in a continuous time sequence and, in reality, there are rich types of longitudinal data to apply our learned model to. To this end, we continually model the AD progression in time sequence via a proposed novel Deep Multi-order Preserving Weight Consolidation (DMoPWC) to simultaneously (1) discover the inter and inner relations among different cognitive measures at different time points and utilize such relations to enhance the learning of associations between imaging features and clinical scores; (2) continually learn new longitudinal patients’ images to overcome forgetting the previously learned knowledge without access to the old data. Moreover, inspired by recent breakthroughs of Recurrent Neural Network, we consider time-order knowledge to further reinforce the statistical power of DMoPWC and ensure features at a particular time will be temporally ahead of the features at its subsequential times. Empirical studies on the longitudinal brain image dataset demonstrate that DMoPWC achieves superior performance over other AD prognosis algorithms.
UR - http://www.scopus.com/inward/record.url?scp=85075661121&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-32245-8_94
DO - 10.1007/978-3-030-32245-8_94
M3 - Conference contribution
AN - SCOPUS:85075661121
SN - 9783030322441
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 850
EP - 859
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
A2 - Shen, Dinggang
A2 - Yap, Pew-Thian
A2 - Liu, Tianming
A2 - Peters, Terry M.
A2 - Khan, Ali
A2 - Staib, Lawrence H.
A2 - Essert, Caroline
A2 - Zhou, Sean
PB - Springer
T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 17 October 2019
ER -