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Joint High-Order Multi-Task Feature Learning to Predict the Progression of Alzheimer’s Disease
- Source :
- Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 ISBN: 9783030009274, MICCAI (1)
- Publication Year :
- 2018
- Publisher :
- Springer International Publishing, 2018.
-
Abstract
- Alzheimer’s disease (AD) is a degenerative brain disease that affects millions of people around the world. As populations in the United States and worldwide age, the prevalence of Alzheimer’s disease will only increase. In turn, the social and financial costs of AD will create a difficult environment for many families and caregivers across the globe. By combining genetic information, brain scans, and clinical data, gathered over time through the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we propose a new Joint High-Order Multi-Modal Multi-Task Feature Learning method to predict the cognitive performance and diagnosis of patients with and without AD.
- Subjects :
- 0301 basic medicine
Financial costs
Gerontology
Neuroimaging
Disease
Sensitivity and Specificity
Article
Pattern Recognition, Automated
Task (project management)
Machine Learning
03 medical and health sciences
0302 clinical medicine
Alzheimer Disease
Image Interpretation, Computer-Assisted
Humans
Cognitive Dysfunction
Effects of sleep deprivation on cognitive performance
High order
Reproducibility of Results
Brain disease
030104 developmental biology
Disease Progression
Psychology
Feature learning
Algorithms
030217 neurology & neurosurgery
Subjects
Details
- ISBN :
- 978-3-030-00927-4
- ISBNs :
- 9783030009274
- Database :
- OpenAIRE
- Journal :
- Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 ISBN: 9783030009274, MICCAI (1)
- Accession number :
- edsair.doi.dedup.....3ff74fb4b5da8a1078cc44c4b3800fa0