Deep learning for neuroimaging-based diagnosis and rehabilitation of autism spectrum disorder: a review
Abstract Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective
rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) …
rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) …
Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review
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 …
symptoms that appear in early childhood. ASD is also associated with communication …
Machine learning based liver disease diagnosis: A systematic review
The computer-based approach is required for the non-invasive detection of chronic liver
diseases that are asymptomatic, progressive, and potentially fatal in nature. In this study, we …
diseases that are asymptomatic, progressive, and potentially fatal in nature. In this study, we …
Retrospective motion artifact correction of structural MRI images using deep learning improves the quality of cortical surface reconstructions
Head motion during MRI acquisition presents significant challenges for neuroimaging
analyses. In this work, we present a retrospective motion correction framework built on a …
analyses. In this work, we present a retrospective motion correction framework built on a …
[HTML][HTML] Automatic brain MRI motion artifact detection based on end-to-end deep learning is similarly effective as traditional machine learning trained on image quality …
P Vakli, B Weiss, J Szalma, P Barsi, I Gyuricza… - Medical Image …, 2023 - Elsevier
Head motion artifacts in magnetic resonance imaging (MRI) are an important confounding
factor concerning brain research as well as clinical practice. For this reason, several …
factor concerning brain research as well as clinical practice. For this reason, several …
Retrospective correction of motion artifact affected structural MRI images using deep learning of simulated motion
Head motion during MRI acquisition presents significant problems for subsequent
neuroimaging analyses. In this work, we propose to use convolutional neural networks …
neuroimaging analyses. In this work, we propose to use convolutional neural networks …
Learning a cortical parcellation of the brain robust to the MRI segmentation with convolutional neural networks
B Thyreau, Y Taki - Medical image analysis, 2020 - Elsevier
The parcellation of the human cortex into meaningful anatomical units is a common step of
various neuroimaging studies. There have been multiple successful efforts to process …
various neuroimaging studies. There have been multiple successful efforts to process …
[HTML][HTML] A supervised learning approach for diffusion MRI quality control with minimal training data
Quality control (QC) is a fundamental component of any study. Diffusion MRI has unique
challenges that make manual QC particularly difficult, including a greater number of artefacts …
challenges that make manual QC particularly difficult, including a greater number of artefacts …
QC-Automator: Deep learning-based automated quality control for diffusion mr images
Quality assessment of diffusion MRI (dMRI) data is essential prior to any analysis, so that
appropriate pre-processing can be used to improve data quality and ensure that the …
appropriate pre-processing can be used to improve data quality and ensure that the …
Automatic detection of motion artifacts on MRI using Deep CNN
Motion artifacts on brain Magnetic Resonance Images (MRI) constitute an important factor
that degrades the image quality, impacting the quantitative analysis based on structural …
that degrades the image quality, impacting the quantitative analysis based on structural …