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Creators/Authors contains: "Elbeleidy, S"

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  1. ; ; ; ; ; ( , Medical Image Computing and Computer Assisted Intervention – MICCAI 2019)
    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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