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

• Published in: IEEE journal of biomedical and health informatics Vol. 24; no. 1; pp. 17 - 26
• Main Authors: Martinez-Murcia, Francisco J., Ortiz, Andres, Gorriz, Juan-Manuel, Ramirez, Javier, Castillo-Barnes, Diego
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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

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...