Chapter title |
New Multi-task Learning Model to Predict Alzheimer’s Disease Cognitive Assessment
|
---|---|
Chapter number | 37 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016
|
Published in |
Lecture notes in computer science, October 2016
|
DOI | 10.1007/978-3-319-46720-7_37 |
Pubmed ID | |
Book ISBNs |
978-3-31-946719-1, 978-3-31-946720-7
|
Authors |
Zhouyuan Huo, Dinggang Shen, Heng Huang, Huo, Zhouyuan, Shen, Dinggang, Huang, Heng, Zhouyuan Huo, Dinggang Shen, Heng Huang |
Editors |
Sebastien Ourselin, Leo Joskowicz, Mert R. Sabuncu, Gozde Unal, William Wells |
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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 29 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 6 | 21% |
Student > Ph. D. Student | 5 | 17% |
Student > Master | 3 | 10% |
Student > Postgraduate | 2 | 7% |
Student > Doctoral Student | 1 | 3% |
Other | 3 | 10% |
Unknown | 9 | 31% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 5 | 17% |
Mathematics | 2 | 7% |
Biochemistry, Genetics and Molecular Biology | 2 | 7% |
Psychology | 2 | 7% |
Social Sciences | 2 | 7% |
Other | 6 | 21% |
Unknown | 10 | 34% |