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Predicting Cognitive Declines Using Longitudinally Enriched Representations for Imaging Biomarkers by Lyujian Lu; Saad Elbeleidy; Lauren Zoe Baker; Hua Wang; Feiping Nie is a Medicine article available to read on EtoBox.

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A critical challenge in using longitudinal neuroimaging data to study the progressions of Alzheimer's Disease (AD) is the varied number of missing records of the patients during the course when AD develops. To tackle this problem, in this paper we propose a novel formulation to learn an enriched representation with fixed length for imaging biomarkers, which aims to simultaneously capture the information conveyed by both baseline neuroimaging record and progressive variations characterized by varied counts of available follow-up records over time. Because the learned biomarker representations are a set of fixed-length vectors, they can be readily used by traditional machine learning models to study AD developments. Take into account that the missing brain scans are not aligned in terms of time in a studied cohort, we develop a new objective that maximizes the ratio of the summations of a number of l -norm distances for improved robustness, which, though, is difficult to efficiently solve in general. Thus, we derive a new efficient and non-greedy iterative solution algorithm and rigorously prove its convergence. We have performed extensive experiments on the Alzheimer's Disease Neuro

Who reads Predicting Cognitive Declines Using Longitudinally Enriched Representations for Imaging Biomarkers?

Author

Lyujian Lu; Saad Elbeleidy; Lauren Zoe Baker; Hua Wang; Feiping Nie

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2020

Language

EN

Field

Medicine (Health Sciences)