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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 newJoint High-Order Multi-Modal Multi-Task Feature Learning method to predict the cognitive performance and diagnosis of patients with and without AD.
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Improved Prediction of Cognitive Outcomes via Globally Aligned Imaging Biomarker Enrichments over Progressions
Incomplete or inconsistent temporal neuroimaging records of patients over time pose a major challenge to accurately predict clinical scores for diagnosing Alzheimer’s Disease (AD). In this paper, we present an unsupervised method to learn enriched imaging biomarker representations that can simultaneously capture the information conveyed by all the baseline neuroimaging measures and the progressive variations of the available follow-up measurements of every participant. Our experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset show improved performance in predicting cognitive outcomes thereby demonstrating the effectiveness of our proposed method.
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- PAR ID:
- 10127254
- Date Published:
- Journal Name:
- Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
- Volume:
- LNCS 11767
- Page Range / eLocation ID:
- 140-148
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-32251-9_16
