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Found 6 papers, 0 papers with code
Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience
Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields.
Robust Hierarchical Patterns for identifying MDD patients: A Multisite Study
Our model can reduce diversity and improve the predictive and generalizing capability of the components.
Learning Robust Hierarchical Patterns of Human Brain across Many fMRI Studies
We also demonstrate the improved reproducibility of the components while preserving age-related variation on a real dataset compiled from multiple sites.
Extraction of Hierarchical Functional Connectivity Components in human brain using Adversarial Learning
The estimation of sparse hierarchical components reflecting patterns of the brain's functional connectivity from rsfMRI data can contribute to our understanding of the brain's functional organization, and can lead to biomarkers of diseases.
Variance Reduced Stochastic Proximal Algorithm for AUC Maximization
To combat this issue, several variance reduced methods have been proposed with faster convergence guarantees than vanilla stochastic gradient descent.
Extraction of hierarchical functional connectivity components in human brain using resting-state fMRI
This paper provides a novel method for the extraction of hierarchical connectivity components in the human brain using resting-state fMRI.
