Computer methods and programs in biomedicine
Mar 2, 2021
To diagnose Alzheimer's disease (AD), neuroimaging methods such as magnetic resonance imaging have been employed. Recent progress in computer vision with deep learning (DL) has further inspired research focused on machine learning algorithms. However...
Cognitive impairments are prevalent in Parkinson's disease (PD), but the underlying mechanisms of their development are unknown. In this study, we aimed to predict global cognition (GC) in PD with machine learning (ML) using structural neuroimaging, ...
Achieving biologically interpretable neural-biomarkers and features from neuroimaging datasets is a challenging task in an MRI-based dyslexia study. This challenge becomes more pronounced when the needed MRI datasets are collected from multiple heter...
OBJECTIVE: Suicide is a priority health problem. Suicide assessment depends on imperfect clinician assessment with minimal ability to predict the risk of suicide. Machine learning/deep learning provides an opportunity to detect an individual at risk ...
In fetal neurosonography, aligning two-dimensional (2D) ultrasound scans to their corresponding plane in the three-dimensional (3D) space remains a challenging task. In this paper, we propose a convolutional neural network that predicts the position ...
Deep learning (DL) methods have been increasingly applied to neuroimaging data to identify patients with psychiatric and neurological disorders. This review provides an overview of the different DL applications within psychiatry and compares DL model...
PURPOSE: The accuracy of existing PET/MR attenuation correction (AC) has been limited by a lack of correlation between MR signal and tissue electron density. Based on our finding that longitudinal relaxation rate, or R , is associated with CT Hounsfi...
IEEE transactions on neural networks and learning systems
Feb 4, 2021
In the context of motor imagery, electroencephalography (EEG) data vary from subject to subject such that the performance of a classifier trained on data of multiple subjects from a specific domain typically degrades when applied to a different subje...
Successful segmentation of the total intracranial vault (ICV) and ventricles is of critical importance when studying neurodegeneration through neuroimaging. We present iCVMapper and VentMapper, robust algorithms that use a convolutional neural networ...
Alzheimer's disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1)...
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