AIMC Topic: Neuroimaging

Clear Filters Showing 331 to 340 of 903 articles

How Machine Learning is Powering Neuroimaging to Improve Brain Health.

Neuroinformatics
This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we areshar...

Deep-learning two-photon fiberscopy for video-rate brain imaging in freely-behaving mice.

Nature communications
Scanning two-photon (2P) fiberscopes (also termed endomicroscopes) have the potential to transform our understanding of how discrete neural activity patterns result in distinct behaviors, as they are capable of high resolution, sub cellular imaging y...

Charting the potential of brain computed tomography deep learning systems.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Brain computed tomography (CTB) scans are widely used to evaluate intracranial pathology. The implementation and adoption of CTB has led to clinical improvements. However, interpretation errors occur and may have substantial morbidity and mortality i...

Reproducible neuroimaging features for diagnosis of autism spectrum disorder with machine learning.

Scientific reports
Autism spectrum disorder (ASD) is the fourth most common neurodevelopmental disorder, with a prevalence of 1 in 160 children. Accurate diagnosis relies on experts, but such individuals are scarce. This has led to increasing interest in the developmen...

Accelerating susceptibility-weighted imaging with deep learning by complex-valued convolutional neural network (ComplexNet): validation in clinical brain imaging.

European radiology
OBJECTIVES: Susceptibility-weighted imaging (SWI) is crucial for the characterization of intracranial hemorrhage and mineralization, but has the drawback of long acquisition times. We aimed to propose a deep learning model to accelerate SWI, and eval...

ArtifactID: Identifying artifacts in low-field MRI of the brain using deep learning.

Magnetic resonance imaging
Low-field MR scanners are more accessible in resource-constrained settings where skilled personnel are scarce. Images acquired in such scenarios are prone to artifacts such as wrap-around and Gibbs ringing. Such artifacts negatively affect the diagno...

Predictive classification of Alzheimer's disease using brain imaging and genetic data.

Scientific reports
For now, Alzheimer's disease (AD) is incurable. But if it can be diagnosed early, the correct treatment can be used to delay the disease. Most of the existing research methods use single or multi-modal imaging features for prediction, relatively few ...

The spectrum of data sharing policies in neuroimaging data repositories.

Human brain mapping
Sharing data is a scientific imperative that accelerates scientific discoveries, reinforces open science inquiry, and allows for efficient use of public investment and research resources. Considering these benefits, data sharing has been widely promo...

Robust-Deep: A Method for Increasing Brain Imaging Datasets to Improve Deep Learning Models' Performance and Robustness.

Journal of digital imaging
A small dataset commonly affects generalization, robustness, and overall performance of deep neural networks (DNNs) in medical imaging research. Since gathering large clinical databases is always difficult, we proposed an analytical method for produc...

Evaluation of Neuro Images for the Diagnosis of Alzheimer's Disease Using Deep Learning Neural Network.

Frontiers in public health
Alzheimer's Disease (AD) is a progressive, neurodegenerative brain disease and is an incurable ailment. No drug exists for AD, but its progression can be delayed if the disorder is identified at its initial stage. Therefore, an early analysis of AD i...