TY - GEN
T1 - Prediction of memory impairment with MRI data
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
AU - Wang, Xiaoqian
AU - Shen, Dinggang
AU - Huang, Heng
N1 - Funding Information:
X. Wang 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 2018 Elsevier B.V., All rights reserved.
PY - 2016
Y1 - 2016
N2 - Alzheimer’s Disease (AD),a severe type of neurodegenerative disorder with progressive impairment of learning and memory,has threatened the health of millions of people. How to recognize AD at early stage is crucial. Multiple models have been presented to predict cognitive impairments by means of neuroimaging data. However,traditional models did not employ the valuable longitudinal information along the progression of the disease. In this paper,we proposed a novel longitudinal feature learning model to simultaneously uncover the interrelations among different cognitive measures at different time points and utilize such interrelated structures to enhance the learning of associations between imaging features and prediction tasks. Moreover,we adopted Schatten p-norm to identify the interrelation structures existing in the low-rank subspace. Empirical results on the ADNI cohort demonstrated promising performance of our model.
AB - Alzheimer’s Disease (AD),a severe type of neurodegenerative disorder with progressive impairment of learning and memory,has threatened the health of millions of people. How to recognize AD at early stage is crucial. Multiple models have been presented to predict cognitive impairments by means of neuroimaging data. However,traditional models did not employ the valuable longitudinal information along the progression of the disease. In this paper,we proposed a novel longitudinal feature learning model to simultaneously uncover the interrelations among different cognitive measures at different time points and utilize such interrelated structures to enhance the learning of associations between imaging features and prediction tasks. Moreover,we adopted Schatten p-norm to identify the interrelation structures existing in the low-rank subspace. Empirical results on the ADNI cohort demonstrated promising performance of our model.
UR - http://www.scopus.com/inward/record.url?scp=84996598828&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84996598828&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-46720-7_32
DO - 10.1007/978-3-319-46720-7_32
M3 - Conference contribution
AN - SCOPUS:84996598828
SN - 9783319467191
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 273
EP - 281
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
Y2 - 21 October 2016 through 21 October 2016
ER -