rs-fMRI and machine learning for ASD diagnosis: a systematic review and meta-analysis
Abstract Autism Spectrum Disorder (ASD) diagnosis is still based on behavioral criteria
through a lengthy and time-consuming process. Much effort is being made to identify brain …
through a lengthy and time-consuming process. Much effort is being made to identify brain …
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 …
[HTML][HTML] A comparative assessment of most widely used machine learning classifiers for analysing and classifying autism spectrum disorder in toddlers and …
Individuals with autism spectrum disorder (ASD) have social interaction and communication
challenges due to a disruption in brain development that impacts how they perceive and …
challenges due to a disruption in brain development that impacts how they perceive and …
Large-scale kernelized granger causality (lskgc) for inferring topology of directed graphs in brain networks
MA Vosoughi, A Wismüller - Medical Imaging 2022 …, 2022 - spiedigitallibrary.org
Graph topology inference in networks with co-evolving and interacting time-series is crucial
for network studies. Vector autoregressive models (VAR) are popular approaches for …
for network studies. Vector autoregressive models (VAR) are popular approaches for …
Non-oscillatory connectivity approach for classification of autism spectrum disorder subtypes using resting-state fMRI
Resting-state functional magnetic resonance imaging (rs-fMRI) is an efficient tool to measure
brain connectivity and it can reveal patterns that distinguish autism spectrum disorder (ASD) …
brain connectivity and it can reveal patterns that distinguish autism spectrum disorder (ASD) …
Large-scale augmented Granger causality (lsAGC) for connectivity analysis in complex systems: From computer simulations to functional MRI (fMRI)
A Wismüller, MA Vosoughi - Medical Imaging 2021 …, 2021 - spiedigitallibrary.org
We introduce large-scale Augmented Granger Causality (lsAGC) as a method for
connectivity analysis in complex systems. The lsAGC algorithm combines dimension …
connectivity analysis in complex systems. The lsAGC algorithm combines dimension …
Anatomical landmark detection in chest x-ray images using transformer-based networks
In this work, we utilize a Transformer-based network for precise anatomical landmark
detection in chest X-ray images. By combining the strengths of Transformers and UNet …
detection in chest X-ray images. By combining the strengths of Transformers and UNet …
An fMRI feature selection method based on a minimum spanning tree for identifying patients with autism
C Shi, J Zhang, X Wu - Symmetry, 2020 - mdpi.com
Autism spectrum disorder (ASD) is a neurodevelopmental disorder originating in infancy and
childhood that may cause language barriers and social difficulties. However, in the …
childhood that may cause language barriers and social difficulties. However, in the …
Detecting landmarks in anatomical medical images using transformer-based networks
Landmark detection is critical in medical imaging for accurate diagnosis and treatment of
diseases. While there are many automated methods for landmark detection, the potential of …
diseases. While there are many automated methods for landmark detection, the potential of …
HLGSNet: Hierarchical and lightweight graph Siamese network with triplet loss for FMRI-based classification of ADHD
Attention Deficit Hyperactivity Disorder (ADHD) is a behavior-based disorder that mainly
occurs in young children. Resting-state fMRI data have been very popular for diagnosing …
occurs in young children. Resting-state fMRI data have been very popular for diagnosing …