AIMC Topic: Brain

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Convolutional Neural Networks for Pediatric Refractory Epilepsy Classification Using Resting-State Functional Magnetic Resonance Imaging.

World neurosurgery
OBJECTIVE: This study aims to evaluate the performance of convolutional neural networks (CNNs) trained with resting-state functional magnetic resonance imaging (rfMRI) latency data in the classification of patients with pediatric epilepsy from health...

Nonhuman rationality: a predictive coding perspective.

Cognitive processing
How can we rethink 'rationality' in the wake of animal and artificial intelligence studies? Can nonhuman systems be rational in any nontrivial sense? In this paper, we propose that all organisms, under certain circumstances, exhibit rationality to a ...

Automated Lateral Ventricular and Cranial Vault Volume Measurements in 13,851 Patients Using Deep Learning Algorithms.

World neurosurgery
BACKGROUND: No large dataset-derived standard has been established for normal or pathologic human cerebral ventricular and cranial vault volumes. Automated volumetric measurements could be used to assist in diagnosis and follow-up of hydrocephalus or...

Brain tumor detection and multi-classification using advanced deep learning techniques.

Microscopy research and technique
A brain tumor is an uncontrolled development of brain cells in brain cancer if not detected at an early stage. Early brain tumor diagnosis plays a crucial role in treatment planning and patients' survival rate. There are distinct forms, properties, a...

Generating Longitudinal Atrophy Evaluation Datasets on Brain Magnetic Resonance Images Using Convolutional Neural Networks and Segmentation Priors.

Neuroinformatics
Brain atrophy quantification plays a fundamental role in neuroinformatics since it permits studying brain development and neurological disorders. However, the lack of a ground truth prevents testing the accuracy of longitudinal atrophy quantification...

Reconstructing seen image from brain activity by visually-guided cognitive representation and adversarial learning.

NeuroImage
Reconstructing perceived stimulus (image) only from human brain activity measured with functional Magnetic Resonance Imaging (fMRI) is a significant task in brain decoding. However, the inconsistent distribution and representation between fMRI signal...

Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal.

NeuroImage
Increasingly large MRI neuroimaging datasets are becoming available, including many highly multi-site multi-scanner datasets. Combining the data from the different scanners is vital for increased statistical power; however, this leads to an increase ...

Automatic brain extraction from 3D fetal MR image with deep learning-based multi-step framework.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Brain extraction is a fundamental prerequisite step in neuroimage analysis for fetus. Due to surrounding maternal tissues and unpredictable movement, brain extraction from fetal Magnetic Resonance (MR) images is a challenging task. In this paper, we ...

Mind Meets Machine: Towards a Cognitive Science of Human-Machine Interactions.

Trends in cognitive sciences
As robots advance from the pages and screens of science fiction into our homes, hospitals, and schools, they are poised to take on increasingly social roles. Consequently, the need to understand the mechanisms supporting human-machine interactions is...

Deep learning detection of informative features in tau PET for Alzheimer's disease classification.

BMC bioinformatics
BACKGROUND: Alzheimer's disease (AD) is the most common type of dementia, typically characterized by memory loss followed by progressive cognitive decline and functional impairment. Many clinical trials of potential therapies for AD have failed, and ...