AIMC Topic: Brain

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Hierarchical binding in convolutional neural networks: Making adversarial attacks geometrically challenging.

Neural networks : the official journal of the International Neural Network Society
We approach the issue of robust machine vision by presenting a novel deep-learning architecture, inspired by work in theoretical neuroscience on how the primate brain performs visual feature binding. Feature binding describes how separately represent...

Multiscale segmentation- and error-guided iterative convolutional neural network for cerebral neuron segmentation in microscopic images.

Microscopy research and technique
This article uses microscopy images obtained from diverse anatomical regions of macaque brain for neuron semantic segmentation. The complex structure of brain, the large intra-class staining intensity difference within neuron class, the small inter-c...

Aberrated Multidimensional EEG Characteristics in Patients with Generalized Anxiety Disorder: A Machine-Learning Based Analysis Framework.

Sensors (Basel, Switzerland)
Although increasing evidences support the notion that psychiatric disorders are associated with abnormal communication between brain regions, scattered studies have investigated brain electrophysiological disconnectivity of patients with generalized ...

Deep Learning to Predict Neonatal and Infant Brain Age from Myelination on Brain MRI Scans.

Radiology
Background Assessment of appropriate brain myelination on T1- and T2-weighted MRI scans is based on gestationally corrected age (GCA) and requires subjective visual inspection of the brain with knowledge of normal myelination milestones. Purpose To d...

Systematic evaluation of machine learning algorithms for neuroanatomically-based age prediction in youth.

Human brain mapping
Application of machine learning (ML) algorithms to structural magnetic resonance imaging (sMRI) data has yielded behaviorally meaningful estimates of the biological age of the brain (brain-age). The choice of the ML approach in estimating brain-age i...

Deep learning with multiresolution handcrafted features for brain MRI segmentation.

Artificial intelligence in medicine
The segmentation of magnetic resonance (MR) images is a crucial task for creating pseudo computed tomography (CT) images which are used to achieve positron emission tomography (PET) attenuation correction. One of the main challenges of creating pseud...

On Spiking Neural Membrane Systems with Neuron and Synapse Creation.

International journal of neural systems
Spiking neural membrane systems are models of computation inspired by the natural functioning of the brain using the concepts of neurons and synapses, and represent a way of building computational systems of a biological inspiration. A variant of suc...

Cognitive Challenges Are Better in Distinguishing Binge From Nonbinge Drinkers: An Exploratory Deep-Learning Study of fMRI Data of Multiple Behavioral Tasks and Resting State.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Studies have identified imaging markers of binge drinking. Functional connectivity during both task challenges and resting state was shown to distinguish binge and nonbinge drinkers. However, no studies have compared the efficacy of task ...

EEG based depression recognition using improved graph convolutional neural network.

Computers in biology and medicine
Depression is a global psychological disease that does serious harm to people. Traditional diagnostic method of the doctor-patient communication, is not objective and accurate enough. Thus, a more accurate and objective method for depression detectio...

SM-SegNet: A Lightweight Squeeze M-SegNet for Tissue Segmentation in Brain MRI Scans.

Sensors (Basel, Switzerland)
In this paper, we propose a novel squeeze M-SegNet (SM-SegNet) architecture featuring a fire module to perform accurate as well as fast segmentation of the brain on magnetic resonance imaging (MRI) scans. The proposed model utilizes uniform input pat...