TY - GEN
T1 - New multi-task learning model to predict Alzheimer’s disease cognitive assessment
AU - Huo, Zhouyuan
AU - Shen, Dinggang
AU - Huang, Heng
N1 - Funding Information:
Z. Huo and H. Huang?were supported in part by NSF IIS-1117965,IIS-1302675,IIS-1344152,DBI-1356628,and NIH AG049371. D. Shen was supported in part by NIH AG041721.
Publisher Copyright:
© Springer International Publishing AG 2016.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2016
Y1 - 2016
N2 - As a neurodegenerative disorder,the Alzheimer’s disease (AD) status can be characterized by the progressive impairment of memory and other cognitive functions. Thus,it is an important topic to use neuroimaging measures to predict cognitive performance and track the progression of AD. Many existing cognitive performance prediction methods employ the regression models to associate cognitive scores to neuroimaging measures,but these methods do not take into account the interconnected structures within imaging data and those among cognitive scores. To address this problem,we propose a novel multi-task learning model for minimizing the k smallest singular values to uncover the underlying low-rank common subspace and jointly analyze all the imaging and clinical data. The effectiveness of our method is demonstrated by the clearly improved prediction performances in all empirical AD cognitive scores prediction cases.
AB - As a neurodegenerative disorder,the Alzheimer’s disease (AD) status can be characterized by the progressive impairment of memory and other cognitive functions. Thus,it is an important topic to use neuroimaging measures to predict cognitive performance and track the progression of AD. Many existing cognitive performance prediction methods employ the regression models to associate cognitive scores to neuroimaging measures,but these methods do not take into account the interconnected structures within imaging data and those among cognitive scores. To address this problem,we propose a novel multi-task learning model for minimizing the k smallest singular values to uncover the underlying low-rank common subspace and jointly analyze all the imaging and clinical data. The effectiveness of our method is demonstrated by the clearly improved prediction performances in all empirical AD cognitive scores prediction cases.
UR - http://www.scopus.com/inward/record.url?scp=84996540025&partnerID=8YFLogxK
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U2 - 10.1007/978-3-319-46720-7_37
DO - 10.1007/978-3-319-46720-7_37
M3 - Conference contribution
AN - SCOPUS:84996540025
SN - 9783319467191
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 317
EP - 325
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
A2 - Unal, Gozde
A2 - Sabuncu, Mert R.
A2 - Wells, William
A2 - Ourselin, Sebastian
A2 - Joskowicz, Leo
PB - Springer Verlag
T2 - 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
Y2 - 21 October 2016 through 21 October 2016
ER -