@MASTERSTHESIS{ 2016:41049864,
 	title = {Identification of autism disorder through functional MRI and deep learning},
 	year = {2016},
 	url = "http://tede2.pucrs.br/tede2/handle/tede/7459",
 	abstract = "Autism Spectrum Disorders (ASD) comprise a range of neurodevelopmental disorders,
 characterized by social deficits and communication difficulties, repetitive behaviors, and cognitive
 delays. The diagnosis of ASD is largely based on behavioral measurements, which can be timeconsuming
 and relies on the patient cooperation and examiner expertise. In order to address this
 limitation, we aim to investigate neural patterns to help in the diagnosis of ASD. In this dissertation,
 we use deep learning techniques to extract robust characteristics from neuroimages of autistic subject
 brain function. Since neuroimage contains about 300,000 spatial points, with approximately 200
 temporal measurements each, deep learning techniques are useful in order to extract important
 features to discriminate ASD subjects from non-ASD. By using denoising autoencoders, a specific
 deep learning technique that aims to reduce data dimensionality, we surpass the state-of-the-art by
 achieving 69% of accuracy, compared to 60% using the same dataset.",
 	publisher = {Pontif?cia Universidade Cat?lica do Rio Grande do Sul},
 	scholl = {Programa de P?s-Gradua??o em Ci?ncia da Computa??o},
 	note = {Faculdade de Inform?tica}
}