Joint High-Order Multi-Task Feature Learning to Predict the Progression of Alzheimer’s Disease
Lodewijk Brand, Hua Wang, Heng Huang, Shannon Risacher, Andrew Saykin, Li Shen, ADNI
MICCAI - 2018
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.
Links
- View publications from Lodewijk Brand
- View publications from Hua Wang
- View publications presented in MICCAI
- View publications in the project, Mining Brain Imaging Genomics Data for Improved Cognitive Health
- View publications in the project, Mining Materials Genome Data for Prediction and Guidance of Nanoparticle Synthesis
- View publications researching Longitudinal / Temporal Learning Models
- View publications researching Multi-Modal/View Data Fusion
- View publications researching Sparsity / Sparse Coding
- View publications applied to Medical Image Computing
Cite this paper
MLA
Brand, Lodewijk, et al. "Joint High-Order Multi-Task Feature Learning to Predict the Progression of Alzheimer’s Disease." International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2018.
BibTeX
@inproceedings{brand2018joint,
title={Joint High-Order Multi-Task Feature Learning to Predict the Progression of Alzheimer's Disease},
author={Brand, Lodewijk and Wang, Hua and Huang, Heng and Risacher, Shannon and Saykin, Andrew and Shen, Li and others},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={555--562},
year={2018},
organization={Springer}
}