AIMC Topic: Brain

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Automatic assessment of Alzheimer's disease diagnosis based on deep learning techniques.

Computers in biology and medicine
Early detection is crucial to prevent the progression of Alzheimer's disease (AD). Thus, specialists can begin preventive treatment as soon as possible. They demand fast and precise assessment in the diagnosis of AD in the earliest and hardest to det...

Accelerating GluCEST imaging using deep learning for B correction.

Magnetic resonance in medicine
PURPOSE: Glutamate weighted Chemical Exchange Saturation Transfer (GluCEST) MRI is a noninvasive technique for mapping parenchymal glutamate in the brain. Because of the sensitivity to field (B ) inhomogeneity, the total acquisition time is prolonged...

Brain tumor classification of virtual NMR voxels based on realistic blood vessel-induced spin dephasing using support vector machines.

NMR in biomedicine
Remodeling of tissue microvasculature commonly promotes neoplastic growth; however, there is no imaging modality in oncology yet that noninvasively quantifies microvascular changes in clinical routine. Although blood capillaries cannot be resolved in...

Brain Medical Image Fusion Based on Dual-Branch CNNs in NSST Domain.

BioMed research international
Computed tomography (CT) images show structural features, while magnetic resonance imaging (MRI) images represent brain tissue anatomy but do not contain any functional information. How to effectively combine the images of the two modes has become a ...

Predicting motor outcome in preterm infants from very early brain diffusion MRI using a deep learning convolutional neural network (CNN) model.

NeuroImage
BACKGROUND AND AIMS: Preterm birth imposes a high risk for developing neuromotor delay. Earlier prediction of adverse outcome in preterm infants is crucial for referral to earlier intervention. This study aimed to predict abnormal motor outcome at 2 ...

A Surrogate Model Based on Artificial Neural Network for RF Radiation Modelling with High-Dimensional Data.

International journal of environmental research and public health
This paper focuses on quantifying the uncertainty in the specific absorption rate valuesof the brain induced by the uncertain positions of the electroencephalography electrodes placed onthe patient's scalp. To avoid running a large number of simulati...

Multiple-target tracking in human and machine vision.

PLoS computational biology
Humans are able to track multiple objects at any given time in their daily activities-for example, we can drive a car while monitoring obstacles, pedestrians, and other vehicles. Several past studies have examined how humans track targets simultaneou...

Artificial Intelligence System Approaching Neuroradiologist-level Differential Diagnosis Accuracy at Brain MRI.

Radiology
Background Although artificial intelligence (AI) shows promise across many aspects of radiology, the use of AI to create differential diagnoses for rare and common diseases at brain MRI has not been demonstrated. Purpose To evaluate an AI system for ...

Multivariate patterns of EEG microstate parameters and their role in the discrimination of patients with schizophrenia from healthy controls.

Psychiatry research
Quasi-stable electrical fields in the EEG, called microstates carry information on the dynamics of large scale brain networks. Using machine learning techniques, we explored whether abnormalities in microstates can be used to classify patients with s...