AIMC Topic: Brain

Clear Filters Showing 3021 to 3030 of 4413 articles

3D whole brain segmentation using spatially localized atlas network tiles.

NeuroImage
Detailed whole brain segmentation is an essential quantitative technique in medical image analysis, which provides a non-invasive way of measuring brain regions from a clinical acquired structural magnetic resonance imaging (MRI). Recently, deep conv...

Arterial Spin Labeling Images Synthesis From sMRI Using Unbalanced Deep Discriminant Learning.

IEEE transactions on medical imaging
Adequate medical images are often indispensable in contemporary deep learning-based medical imaging studies, although the acquisition of certain image modalities may be limited due to several issues including high costs and patients issues. However, ...

Organic Synapses for Neuromorphic Electronics: From Brain-Inspired Computing to Sensorimotor Nervetronics.

Accounts of chemical research
Living organisms have a long evolutionary history that has provided them with functions and structures that enable them to survive in their environment. The goal of biomimetic technology is to emulate these traits of living things. Research in bioins...

MTBI Identification From Diffusion MR Images Using Bag of Adversarial Visual Features.

IEEE transactions on medical imaging
In this paper, we propose bag of adversarial features (BAFs) for identifying mild traumatic brain injury (MTBI) patients from their diffusion magnetic resonance images (MRIs) (obtained within one month of injury) by incorporating unsupervised feature...

Data-driven synthetic MRI FLAIR artifact correction via deep neural network.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: FLAIR (fluid attenuated inversion recovery) imaging via synthetic MRI methods leads to artifacts in the brain, which can cause diagnostic limitations. The main sources of the artifacts are attributed to the partial volume effect and flow,...

Semisupervised Deep Stacking Network with Adaptive Learning Rate Strategy for Motor Imagery EEG Recognition.

Neural computation
Practical motor imagery electroencephalogram (EEG) data-based applications are limited by the waste of unlabeled samples in supervised learning and excessive time consumption in the pretraining period. A semisupervised deep stacking network with an a...

Brain Morphometry Methods for Feature Extraction in Random Subspace Ensemble Neural Network Classification of First-Episode Schizophrenia.

Neural computation
Machine learning (ML) is a growing field that provides tools for automatic pattern recognition. The neuroimaging community currently tries to take advantage of ML in order to develop an auxiliary diagnostic tool for schizophrenia diagnostics. In this...

Deep neural network models of sensory systems: windows onto the role of task constraints.

Current opinion in neurobiology
Sensory neuroscience aims to build models that predict neural responses and perceptual behaviors, and that provide insight into the principles that give rise to them. For decades, artificial neural networks trained to perform perceptual tasks have at...

Deep Collaborative Learning With Application to the Study of Multimodal Brain Development.

IEEE transactions on bio-medical engineering
OBJECTIVE: Multi-modal functional magnetic resonance imaging has been widely used for brain research. Conventional data-fusion methods cannot capture complex relationship (e.g., nonlinear predictive relationship) between multiple data. This paper aim...

Towards the neural population doctrine.

Current opinion in neurobiology
Across neuroscience, large-scale data recording and population-level analysis methods have experienced explosive growth. While the underlying hardware and computational techniques have been well reviewed, we focus here on the novel science that these...