AIMC Topic: Brain

Clear Filters Showing 2401 to 2410 of 4413 articles

Stochastic synchronization of dynamics on the human connectome.

NeuroImage
Synchronization is a collective mechanism by which oscillatory networks achieve their functions. Factors driving synchronization include the network's topological and dynamical properties. However, how these factors drive the emergence of synchroniza...

Deep learning identifies partially overlapping subnetworks in the human social brain.

Communications biology
Complex social interplay is a defining property of the human species. In social neuroscience, many experiments have sought to first define and then locate 'perspective taking', 'empathy', and other psychological concepts to specific brain circuits. S...

4D deep image prior: dynamic PET image denoising using an unsupervised four-dimensional branch convolutional neural network.

Physics in medicine and biology
Although convolutional neural networks (CNNs) demonstrate the superior performance in denoising positron emission tomography (PET) images, a supervised training of the CNN requires a pair of large, high-quality PET image datasets. As an unsupervised ...

An optimal brain tumor detection by convolutional neural network and Enhanced Sparrow Search Algorithm.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
Precise and timely detection of brain tumor area has a very high effect on the selection of medical care, its success rate and following the disease process during treatment. Existing algorithms for brain tumor diagnosis have problems in terms of bet...

Automatic segmentation of white matter hyperintensities from brain magnetic resonance images in the era of deep learning and big data - A systematic review.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
BACKGROUND: White matter hyperintensities (WMH), of presumed vascular origin, are visible and quantifiable neuroradiological markers of brain parenchymal change. These changes may range from damage secondary to inflammation and other neurological con...

Flexible boron nitride-based memristor for in situ digital and analogue neuromorphic computing applications.

Materials horizons
The data processing efficiency of traditional computers is suffering from the intrinsic limitation of physically separated processing and memory units. Logic-in-memory and brain-inspired neuromorphic computing are promising in-memory computing paradi...

Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning.

Nature communications
Recent critical commentaries unfavorably compare deep learning (DL) with standard machine learning (SML) approaches for brain imaging data analysis. However, their conclusions are often based on pre-engineered features depriving DL of its main advant...

Generation of Brain Dual-Energy CT from Single-Energy CT Using Deep Learning.

Journal of digital imaging
Deep learning (DL) has shown great potential in conversions between various imaging modalities. Similarly, DL can be applied to synthesize a high-kV computed tomography (CT) image from its corresponding low-kV CT image. This indicates the feasibility...

Improved amyloid burden quantification with nonspecific estimates using deep learning.

European journal of nuclear medicine and molecular imaging
PURPOSE: Standardized uptake value ratio (SUVr) used to quantify amyloid-β burden from amyloid-PET scans can be biased by variations in the tracer's nonspecific (NS) binding caused by the presence of cerebrovascular disease (CeVD). In this work, we p...

Performance of deep learning synthetic CTs for MR-only brain radiation therapy.

Journal of applied clinical medical physics
PURPOSE: To evaluate the dosimetric and image-guided radiation therapy (IGRT) performance of a novel generative adversarial network (GAN) generated synthetic CT (synCT) in the brain and compare its performance for clinical use including conventional ...