A semi-supervised autoencoder for autism disease diagnosis
Autism spectrum disorder (ASD) is a neurological developmental disorder that typically
causes impaired communication and compromised social interactions. The current clinical …
causes impaired communication and compromised social interactions. The current clinical …
Automated image quality evaluation of structural brain MRI using an ensemble of deep learning networks
Background Deep learning (DL) is a promising methodology for automatic detection of
abnormalities in brain MRI. Purpose To automatically evaluate the quality of multicenter …
abnormalities in brain MRI. Purpose To automatically evaluate the quality of multicenter …
Pediatric postoperative cerebellar cognitive affective syndrome follows outflow pathway lesions
Objective To evaluate lesion location after pediatric cerebellar tumor resection in relation to
the development of severe cognitive and affective disturbances, or cerebellar cognitive …
the development of severe cognitive and affective disturbances, or cerebellar cognitive …
Combining phenotypic and resting-state fMRI data for autism classification with recurrent neural networks
NC Dvornek, P Ventola… - 2018 IEEE 15th …, 2018 - ieeexplore.ieee.org
Accurate identification of autism spectrum disorder (ASD) from resting-state functional
magnetic resonance imaging (rsfMRI) is a challenging task due in large part to the …
magnetic resonance imaging (rsfMRI) is a challenging task due in large part to the …
Deep fusion of multi-template using spatio-temporal weighted multi-hypergraph convolutional networks for brain disease analysis
Conventional functional connectivity network (FCN) based on resting-state fMRI (rs-fMRI)
can only reflect the relationship between pairwise brain regions. Thus, the hyper …
can only reflect the relationship between pairwise brain regions. Thus, the hyper …
FAIRly big: A framework for computationally reproducible processing of large-scale data
Large-scale datasets present unique opportunities to perform scientific investigations with
unprecedented breadth. However, they also pose considerable challenges for the findability …
unprecedented breadth. However, they also pose considerable challenges for the findability …
The role of structure MRI in diagnosing autism
This study proposes a Computer-Aided Diagnostic (CAD) system to diagnose subjects with
autism spectrum disorder (ASD). The CAD system identifies morphological anomalies within …
autism spectrum disorder (ASD). The CAD system identifies morphological anomalies within …
Single volume image generator and deep learning-based ASD classification
MR Ahmed, Y Zhang, Y Liu… - IEEE Journal of Biomedical …, 2020 - ieeexplore.ieee.org
Autism spectrum disorder (ASD) is an intricate neuropsychiatric brain disorder characterized
by social deficits and repetitive behaviors. Deep learning approaches have been applied in …
by social deficits and repetitive behaviors. Deep learning approaches have been applied in …
Plsnet: Position-aware gcn-based autism spectrum disorder diagnosis via fc learning and rois sifting
Brain function connectivity, derived from functional magnetic resonance imaging (fMRI), has
enjoyed high popularity in the studies of Autism Spectrum Disorder (ASD) diagnosis. Albeit …
enjoyed high popularity in the studies of Autism Spectrum Disorder (ASD) diagnosis. Albeit …
ECNN: Enhanced convolutional neural network for efficient diagnosis of autism spectrum disorder
R Kashef - Cognitive Systems Research, 2022 - Elsevier
This paper aims to apply deep learning to identify autism spectrum disorder (ASD) patients
from a large brain imaging dataset based on the patients' brain activation patterns. The brain …
from a large brain imaging dataset based on the patients' brain activation patterns. The brain …