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Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis
Joint Authors
Cárdenas-Peña, David
Collazos-Huertas, Diego
Castellanos-Dominguez, German
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-04-11
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Dementia is a growing problem that affects elderly people worldwide.
More accurate evaluation of dementia diagnosis can help during the medical examination.
Several methods for computer-aided dementia diagnosis have been proposed using resonance imaging scans to discriminate between patients with Alzheimer’s disease (AD) or mild cognitive impairment (MCI) and healthy controls (NC).
Nonetheless, the computer-aided diagnosis is especially challenging because of the heterogeneous and intermediate nature of MCI.
We address the automated dementia diagnosis by introducing a novel supervised pretraining approach that takes advantage of the artificial neural network (ANN) for complex classification tasks.
The proposal initializes an ANN based on linear projections to achieve more discriminating spaces.
Such projections are estimated by maximizing the centered kernel alignment criterion that assesses the affinity between the resonance imaging data kernel matrix and the label target matrix.
As a result, the performed linear embedding allows accounting for features that contribute the most to the MCI class discrimination.
We compare the supervised pretraining approach to two unsupervised initialization methods (autoencoders and Principal Component Analysis) and against the best four performing classification methods of the 2014 CADDementia challenge.
As a result, our proposal outperforms all the baselines (7% of classification accuracy and area under the receiver-operating-characteristic curve) at the time it reduces the class biasing.
American Psychological Association (APA)
Cárdenas-Peña, David& Collazos-Huertas, Diego& Castellanos-Dominguez, German. 2016. Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis. Computational and Mathematical Methods in Medicine،Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1100238
Modern Language Association (MLA)
Cárdenas-Peña, David…[et al.]. Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis. Computational and Mathematical Methods in Medicine No. 2016 (2016), pp.1-10.
https://search.emarefa.net/detail/BIM-1100238
American Medical Association (AMA)
Cárdenas-Peña, David& Collazos-Huertas, Diego& Castellanos-Dominguez, German. Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis. Computational and Mathematical Methods in Medicine. 2016. Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1100238
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1100238