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New Multi-task Learning Model to Predict Alzheimer’s Disease Cognitive Assessment
- Source :
- Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016 ISBN: 9783319467191, MICCAI (1)
- Publication Year :
- 2016
- Publisher :
- Springer International Publishing, 2016.
-
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.
- Subjects :
- Computer science
Multi-task learning
Neuroimaging
02 engineering and technology
Disease
Sensitivity and Specificity
Article
03 medical and health sciences
0302 clinical medicine
Alzheimer Disease
0202 electrical engineering, electronic engineering, information engineering
medicine
Humans
Cognitive Dysfunction
Effects of sleep deprivation on cognitive performance
Reproducibility of Results
Cognition
medicine.disease
Disease Progression
Regression Analysis
020201 artificial intelligence & image processing
Cognitive Assessment System
Alzheimer's disease
Algorithms
030217 neurology & neurosurgery
Cognitive psychology
Subjects
Details
- ISBN :
- 978-3-319-46719-1
- ISBNs :
- 9783319467191
- Database :
- OpenAIRE
- Journal :
- Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016 ISBN: 9783319467191, MICCAI (1)
- Accession number :
- edsair.doi.dedup.....fbbdc7d2094071bc0c5239a969c2bc0c