[HTML][HTML] Population graph-based multi-model ensemble method for diagnosing autism spectrum disorder
With the advancement of brain imaging techniques and a variety of machine learning
methods, significant progress has been made in brain disorder diagnosis, in particular …
methods, significant progress has been made in brain disorder diagnosis, in particular …
Collaborative learning of graph generation, clustering and classification for brain networks diagnosis
Purpose: Accurate diagnosis of autism spectrum disorder (ASD) plays a key role in
improving the condition and quality of life for patients. In this study, we mainly focus on ASD …
improving the condition and quality of life for patients. In this study, we mainly focus on ASD …
Bootstrapping graph convolutional neural networks for autism spectrum disorder classification
R Anirudh, JJ Thiagarajan - ICASSP 2019-2019 IEEE …, 2019 - ieeexplore.ieee.org
Using predictive models to identify patterns that can act as biomarkers for different
neuropathoglogical conditions is becoming highly prevalent. In this paper, we consider the …
neuropathoglogical conditions is becoming highly prevalent. In this paper, we consider the …
Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer's disease
Graphs are widely used as a natural framework that captures interactions between
individual elements represented as nodes in a graph. In medical applications, specifically …
individual elements represented as nodes in a graph. In medical applications, specifically …
[HTML][HTML] Detection of autism spectrum disorder using graph representation learning algorithms and deep neural network, based on fMRI signals
A Yousefian, F Shayegh, Z Maleki - Frontiers in Systems …, 2023 - frontiersin.org
Introduction Can we apply graph representation learning algorithms to identify autism
spectrum disorder (ASD) patients within a large brain imaging dataset? ASD is mainly …
spectrum disorder (ASD) patients within a large brain imaging dataset? ASD is mainly …
TE-HI-GCN: An ensemble of transfer hierarchical graph convolutional networks for disorder diagnosis
Accurate diagnosis of psychiatric disorders plays a critical role in improving the quality of life
for patients and potentially supports the development of new treatments. Graph …
for patients and potentially supports the development of new treatments. Graph …
[HTML][HTML] Enhancing diagnosis of autism with optimized machine learning models and personal characteristic data
Autism spectrum disorder (ASD) is a developmental disorder, affecting about 1% of the
global population. Currently, the only clinical method for diagnosing ASD are standardized …
global population. Currently, the only clinical method for diagnosing ASD are standardized …
Modeling the dynamic brain network representation for autism spectrum disorder diagnosis
The dynamic functional connectivity analysis provides valuable information for
understanding functional brain activity underlying different cognitive processes. Modeling …
understanding functional brain activity underlying different cognitive processes. Modeling …
Multi-atlas graph convolutional networks and convolutional recurrent neural networks-based ensemble learning for classification of autism spectrum disorders
Autism spectrum disorder (ASD) has an influence on social conversation and interaction, as
well as encouraging people to engage in repetitive behaviors. The complication begins in …
well as encouraging people to engage in repetitive behaviors. The complication begins in …
Using DeepGCN to identify the autism spectrum disorder from multi-site resting-state data
M Cao, M Yang, C Qin, X Zhu, Y Chen, J Wang… - … Signal Processing and …, 2021 - Elsevier
It is challenging to discriminate Autism spectrum disorder (ASD) from a highly
heterogeneous database, because there is a great deal of uncontrollable variability in the …
heterogeneous database, because there is a great deal of uncontrollable variability in the …