Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

P Moridian, N Ghassemi, M Jafari… - Frontiers in Molecular …, 2022‏ - frontiersin.org
Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and
symptoms that appear in early childhood. ASD is also associated with communication …

Salience processing and insular cortical function and dysfunction

LQ Uddin - Nature reviews neuroscience, 2015‏ - nature.com
The brain is constantly bombarded by stimuli, and the relative salience of these inputs
determines which are more likely to capture attention. A brain system known as the'salience …

[PDF][PDF] Explanations based on the missing: Towards contrastive explanations with pertinent negatives

A Dhurandhar, PY Chen, R Luss… - Advances in neural …, 2018‏ - proceedings.neurips.cc
In this paper we propose a novel method that provides contrastive explanations justifying the
classification of an input by a black box classifier such as a deep neural network. Given an …

MS-Net: multi-site network for improving prostate segmentation with heterogeneous MRI data

Q Liu, Q Dou, L Yu, PA Heng - IEEE transactions on medical …, 2020‏ - ieeexplore.ieee.org
Automated prostate segmentation in MRI is highly demanded for computer-assisted
diagnosis. Recently, a variety of deep learning methods have achieved remarkable progress …

[HTML][HTML] Identification of autism spectrum disorder using deep learning and the ABIDE dataset

AS Heinsfeld, AR Franco, RC Craddock… - NeuroImage: Clinical, 2018‏ - Elsevier
The goal of the present study was to apply deep learning algorithms to identify autism
spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the …

Resting-state connectivity biomarkers define neurophysiological subtypes of depression

AT Drysdale, L Grosenick, J Downar, K Dunlop… - Nature medicine, 2017‏ - nature.com
Biomarkers have transformed modern medicine but remain largely elusive in psychiatry,
partly because there is a weak correspondence between diagnostic labels and their …

ASD-DiagNet: a hybrid learning approach for detection of autism spectrum disorder using fMRI data

T Eslami, V Mirjalili, A Fong, AR Laird… - Frontiers in …, 2019‏ - frontiersin.org
Heterogeneous mental disorders such as Autism Spectrum Disorder (ASD) are notoriously
difficult to diagnose, especially in children. The current psychiatric diagnostic process is …

Enhancing studies of the connectome in autism using the autism brain imaging data exchange II

A Di Martino, D O'connor, B Chen, K Alaerts… - Scientific data, 2017‏ - nature.com
The second iteration of the Autism Brain Imaging Data Exchange (ABIDE II) aims to enhance
the scope of brain connectomics research in Autism Spectrum Disorder (ASD). Consistent …

Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example

A Abraham, MP Milham, A Di Martino, RC Craddock… - NeuroImage, 2017‏ - Elsevier
Abstract Resting-state functional Magnetic Resonance Imaging (R-fMRI) holds the promise
to reveal functional biomarkers of neuropsychiatric disorders. However, extracting such …

Automated detection of autism spectrum disorder using a convolutional neural network

Z Sherkatghanad, M Akhondzadeh, S Salari… - Frontiers in …, 2020‏ - frontiersin.org
Background: Convolutional neural networks (CNN) have enabled significant progress in
speech recognition, image classification, automotive software engineering, and …