Objective Machine learning (ML) has been widely used to detect and evaluate major
depressive disorder (MDD) using neuroimaging data, ie, resting-state functional magnetic …

Schizophrenia is a socially significant mental disorder resulting frequently in severe forms of
disability. Diagnosis, choice of treatment tactics, and rehabilitation in clinical psychiatry are …

Mild cognitive impairment (MCI) is an important public health problem that has enormous
consequences for patients, their families, the health care system, and the economy. MCI is …

In recent years there has been a rapid increase in classification methods on graph
structured data. Both in graph kernels and graph neural networks, one of the implicit …

Nowadays, Software-Defined Networks (SDNs) are increasingly being used in many
practical settings, posing a variety of security risks, such as compromised switches. Once a …

Automatic segmentation methods based on deep learning have recently demonstrated state-
of-the-art performance, outperforming the ordinary methods. Nevertheless, these methods …

Deep learning convolution neural networks have proved to be a powerful tool for MRI
analysis. In current work, we explore the potential of the deformable convolution deep neural …

Objective Resting-state functional connectivity reveals a promising way for the early
detection of dementia. This study proposes a novel method to accurately classify Healthy …

Major Depression Disorder (MDD) affects people's life and it is a common disorder
worldwide. Finding useful diagnostic biomarkers would help clinicians to diagnosis MDD in …

Electroencephalogram (EEG) is a set of time series each of which can be represented as a
2D image (spectrogram), so that EEG recording can be mapped to the C-dimensional image …