AIMC Topic: Brain

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Jointly estimating parametric maps of multiple diffusion models from undersampled q-space data: A comparison of three deep learning approaches.

Magnetic resonance in medicine
PURPOSE: While advanced diffusion techniques have been found valuable in many studies, their clinical availability has been hampered partly due to their long scan times. Moreover, each diffusion technique can only extract a few relevant microstructur...

Attention-guided deep learning for gestational age prediction using fetal brain MRI.

Scientific reports
Magnetic resonance imaging offers unrivaled visualization of the fetal brain, forming the basis for establishing age-specific morphologic milestones. However, gauging age-appropriate neural development remains a difficult task due to the constantly c...

Brain tumour classification of magnetic resonance images using a novel CNN-based medical image analysis and detection network in comparison to VGG16.

Journal of population therapeutics and clinical pharmacology = Journal de la therapeutique des populations et de la pharmacologie clinique
AIM: This study aims at developing an automatic medical image analysis and detection for accurate classification of brain tumors from MRI dataset. The study implemented our novel MIDNet18 CNN architecture in comparison with the VGG16 CNN architecture...

Integrated deep learning framework for accelerated optical coherence tomography angiography.

Scientific reports
Label-free optical coherence tomography angiography (OCTA) has become a premium imaging tool in clinics to obtain structural and functional information of microvasculatures. One primary technical drawback for OCTA, however, is its imaging speed. The ...

A self-supervised domain-general learning framework for human ventral stream representation.

Nature communications
Anterior regions of the ventral visual stream encode substantial information about object categories. Are top-down category-level forces critical for arriving at this representation, or can this representation be formed purely through domain-general ...

MDReg-Net: Multi-resolution diffeomorphic image registration using fully convolutional networks with deep self-supervision.

Human brain mapping
We present a diffeomorphic image registration algorithm to learn spatial transformations between pairs of images to be registered using fully convolutional networks (FCNs) under a self-supervised learning setting. Particularly, a deep neural network ...

DOTA: Deep Learning Optimal Transport Approach to Advance Drug Repositioning for Alzheimer's Disease.

Biomolecules
Alzheimer's disease (AD) is the leading cause of age-related dementia, affecting over 5 million people in the United States and incurring a substantial global healthcare cost. Unfortunately, current treatments are only palliative and do not cure AD. ...

A gradient-based automatic optimization CNN framework for EEG state recognition.

Journal of neural engineering
. The electroencephalogram (EEG) signal, as a data carrier that can contain a large amount of information about the human brain in different states, is one of the most widely used metrics for assessing human psychophysiological states. Among a variet...

Evaluation of deep convolutional neural networks for in situ hybridization gene expression image representation.

PloS one
High resolution in situ hybridization (ISH) images of the brain capture spatial gene expression at cellular resolution. These spatial profiles are key to understanding brain organization at the molecular level. Previously, manual qualitative scoring ...

Individualized spatial network predictions using Siamese convolutional neural networks: A resting-state fMRI study of over 11,000 unaffected individuals.

PloS one
Individuals can be characterized in a population according to their brain measurements and activity, given the inter-subject variability in brain anatomy, structure-function relationships, or life experience. Many neuroimaging studies have demonstrat...