Studying the Manifold Structure of Alzheimer's Disease: A Deep Learning Approach Using Convolutional Autoencoders

Many classical machine learning techniques have been used to explore Alzheimer's disease (AD), evolving from image decomposition techniques such as principal component analysis toward higher complexity, non-linear decomposition algorithms. With the arrival of the deep learning paradigm, it has...

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Veröffentlicht in:	IEEE journal of biomedical and health informatics Jg. 24; H. 1; S. 17 - 26
Hauptverfasser:	Martinez-Murcia, Francisco J., Ortiz, Andres, Gorriz, Juan-Manuel, Ramirez, Javier, Castillo-Barnes, Diego
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Alzheimer Disease - classification Alzheimer Disease - diagnosis Alzheimer Disease - pathology Alzheimer Disease - physiopathology Alzheimer's disease Brain - diagnostic imaging Brain - pathology Classification Cognitive ability Cognitive Dysfunction - classification Cognitive Dysfunction - diagnosis Cognitive Dysfunction - pathology Cognitive Dysfunction - physiopathology Computer architecture convolutional autoencoder Data analysis data fusion Databases, Factual Decomposition Deep Learning Diagnosis, Computer-Assisted - methods Disease Progression Feature extraction Humans Information processing Learning algorithms Machine learning Magnetic Resonance Imaging manifold learning Manifolds Medical imaging Mental Status and Dementia Tests Neurodegeneration Neurodegenerative diseases Neuroimaging Principal components analysis Regression analysis Severity of Illness Index Signs and symptoms
ISSN:	2168-2194, 2168-2208, 2168-2208
Online-Zugang:	Volltext
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