AIMC Topic: Brain

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Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture.

Medical hypotheses
Brain tumor is one of the dangerous and deadly cancer types seen in adults and children. Early and accurate diagnosis of brain tumor is important for the treatment process. It is an important step for specialists to detect the brain tumor using compu...

Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities.

NMR in biomedicine
Quantitative susceptibility mapping (QSM) has gained broad interest in the field by extracting bulk tissue magnetic susceptibility, predominantly determined by myelin, iron and calcium from magnetic resonance imaging (MRI) phase measurements in vivo....

Automated Brain Metastases Detection Framework for T1-Weighted Contrast-Enhanced 3D MRI.

IEEE journal of biomedical and health informatics
Brain Metastases (BM) complicate 20-40% of cancer cases. BM lesions can present as punctate (1 mm) foci, requiring high-precision Magnetic Resonance Imaging (MRI) in order to prevent inadequate or delayed BM treatment. However, BM lesion detection re...

Deep Neural Oracles for Short-Window Optimized Compressed Sensing of Biosignals.

IEEE transactions on biomedical circuits and systems
The recovery of sparse signals given their linear mapping on lower-dimensional spaces can be partitioned into a support estimation phase and a coefficient estimation phase. We propose to estimate the support with an oracle based on a deep neural netw...

Brain morphological changes in hypokinetic dysarthria of Parkinson's disease and use of machine learning to predict severity.

CNS neuroscience & therapeutics
BACKGROUND: Up to 90% of patients with Parkinson's disease (PD) eventually develop the speech and voice disorder referred to as hypokinetic dysarthria (HD). However, the brain morphological changes associated with HD have not been investigated. Moreo...

Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions.

Bipolar disorders
OBJECTIVES: The existence of anatomofunctional brain abnormalities in bipolar disorder (BD) is now well established by magnetic resonance imaging (MRI) studies. To create diagnostic and prognostic tools, as well as identifying biologically valid subt...

A multistep deep learning framework for the automated detection and segmentation of astrocytes in fluorescent images of brain tissue.

Scientific reports
While astrocytes have been traditionally described as passive supportive cells, studies during the last decade have shown they are active players in many aspects of CNS physiology and function both in normal and disease states. However, the precise m...

Feature optimization method for machine learning-based diagnosis of schizophrenia using magnetoencephalography.

Journal of neuroscience methods
BACKGROUND: When many features and a small number of clinical data exist, previous studies have used a few top-ranked features from the Fisher's discriminant ratio (FDR) for feature selection. However, there are many similarities between selected fea...

Brain-Controlled Robotic Arm System Based on Multi-Directional CNN-BiLSTM Network Using EEG Signals.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Brain-machine interfaces (BMIs) can be used to decode brain activity into commands to control external devices. This paper presents the decoding of intuitive upper extremity imagery for multi-directional arm reaching tasks in three-dimensional (3D) e...

Before and beyond the Wilson-Cowan equations.

Journal of neurophysiology
The Wilson-Cowan equations represent a landmark in the history of computational neuroscience. Along with the insights Wilson and Cowan offered for neuroscience, they crystallized an approach to modeling neural dynamics and brain function. Although th...