Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings |
| Publisher | Springer Verlag |
| Pages | 317-325 |
| Number of pages | 9 |
| Volume | 9900 LNCS |
| ISBN (Print) | 9783319467191 |
| DOIs | |
| Publication status | Published - 2016 |
| Externally published | Yes |
| Event | 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 - Athens, Greece Duration: 2016 Oct 21 → 2016 Oct 21 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 9900 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 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 |
|---|---|
| Country | Greece |
| City | Athens |
| Period | 16/10/21 → 16/10/21 |
Fingerprint
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
New multi-task learning model to predict Alzheimer’s disease cognitive assessment. / Huo, Zhouyuan; Shen, Dinggang; Huang, Heng.
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Vol. 9900 LNCS Springer Verlag, 2016. p. 317-325 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9900 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - New multi-task learning model to predict Alzheimer’s disease cognitive assessment
AU - Huo, Zhouyuan
AU - Shen, Dinggang
AU - Huang, Heng
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
UR - http://www.scopus.com/inward/citedby.url?scp=84996540025&partnerID=8YFLogxK
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
VL - 9900 LNCS
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
PB - Springer Verlag
ER -