AIMC Topic: Brain

Clear Filters Showing 2111 to 2120 of 4413 articles

Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons.

Nature communications
In order to better understand how the brain perceives faces, it is important to know what objective drives learning in the ventral visual stream. To answer this question, we model neural responses to faces in the macaque inferotemporal (IT) cortex wi...

Diagnosis of Schizophrenia Based on Deep Learning Using fMRI.

Computational and mathematical methods in medicine
Schizophrenia is a brain disease that frequently occurs in young people. Early diagnosis and treatment can reduce family burdens and reduce social costs. There is no objective evaluation index for schizophrenia. In order to improve the classification...

High-Throughput, Label-Free and Slide-Free Histological Imaging by Computational Microscopy and Unsupervised Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Rapid and high-resolution histological imaging with minimal tissue preparation has long been a challenging and yet captivating medical pursuit. Here, the authors propose a promising and transformative histological imaging method, termed computational...

Improving Sensitivity of Arterial Spin Labeling Perfusion MRI in Alzheimer's Disease Using Transfer Learning of Deep Learning-Based ASL Denoising.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Arterial spin labeling (ASL) perfusion magnetic resonance imaging (MRI) denoising through deep learning (DL) often faces insufficient training data from patients. One solution is to train DL models using healthy subjects' data which are m...

Minimum spanning tree based graph neural network for emotion classification using EEG.

Neural networks : the official journal of the International Neural Network Society
Emotion classification based on neurophysiology signals has been a challenging issue in the literature. Recent neuroscience findings suggest that brain network structure underlying the different emotions provides a window in understanding human affec...

Machine Learning for Predicting Motor Improvement After Acute Subcortical Infarction Using Baseline Whole Brain Volumes.

Neurorehabilitation and neural repair
Neuroimaging biomarkers are valuable predictors of motor improvement after stroke, but there is a gap between published evidence and clinical usage. In this work, we aimed to investigate whether machine learning techniques, when applied to a combin...

Stochastic Spiking Behavior in Neuromorphic Networks Enables True Random Number Generation.

ACS applied materials & interfaces
There is currently a great deal of interest in the use of nanoscale devices to emulate the behaviors of neurons and synapses and to facilitate brain-inspired computation. Here, it is shown that percolating networks of nanoparticles exhibit stochastic...

QQ-NET - using deep learning to solve quantitative susceptibility mapping and quantitative blood oxygen level dependent magnitude (QSM+qBOLD or QQ) based oxygen extraction fraction (OEF) mapping.

Magnetic resonance in medicine
PURPOSE: To improve accuracy and speed of quantitative susceptibility mapping plus quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) -based oxygen extraction fraction (OEF) mapping using a deep neural network (QQ-NET).

3D hemisphere-based convolutional neural network for whole-brain MRI segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Whole-brain segmentation is a crucial pre-processing step for many neuroimaging analyses pipelines. Accurate and efficient whole-brain segmentations are important for many neuroimage analysis tasks to provide clinically relevant information. Several ...

Deep learning for neuroimaging-based diagnosis and rehabilitation of Autism Spectrum Disorder: A review.

Computers in biology and medicine
Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) techniques can aid physicians to apply automatic diagnosis and rehabilitation pro...