AIMC Topic: Brain
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Validation of SynthSeg segmentation performance on CT using paired MRI from radiotherapy patients.
NeuroImage
Nov 16, 2024
INTRODUCTION: Manual segmentation of medical images is labor intensive and especially challenging for images with poor contrast or resolution. The presence of disease exacerbates this further, increasing the need for an automated solution. To this ex...
An intelligent magnetic resonance imagining-based multistage Alzheimer's disease classification using swish-convolutional neural networks.
Medical & biological engineering & computing
Nov 15, 2024
Alzheimer's disease (AD) refers to a neurological disorder that causes damage to brain cells and results in decreasing cognitive abilities and memory. In brain scans, this degeneration can be seen in different ways. The disease can be classified into...
Deep learning of structural MRI predicts fluid, crystallized, and general intelligence.
Scientific reports
Nov 14, 2024
Can brain structure predict human intelligence? T1-weighted structural brain magnetic resonance images (sMRI) have been correlated with intelligence. However, the population-level association does not fully account for individual variability in intel...
Generalizable self-supervised learning for brain CTA in acute stroke.
Computers in biology and medicine
Nov 12, 2024
Acute stroke management involves rapid and accurate interpretation of CTA imaging data. However, generalizable models for multiple acute stroke tasks able to learn from unlabeled data do not exist. We propose a linear probed self-supervised contrasti...
Recognizing and explaining driving stress using a Shapley additive explanation model by fusing EEG and behavior signals.
Accident; analysis and prevention
Nov 12, 2024
Driving stress is a critical factor leading to road traffic accidents. Despite numerous studies that have been conducted on driving stress recognition, most of them only focus on accuracy improvement without taking model interpretability into account...
Analysis of behavioral flow resolves latent phenotypes.
Nature methods
Nov 12, 2024
The accurate detection and quantification of rodent behavior forms a cornerstone of basic biomedical research. Current data-driven approaches, which segment free exploratory behavior into clusters, suffer from low statistical power due to multiple te...
Brain imaging and machine learning reveal uncoupled functional network for contextual threat memory in long sepsis.
Scientific reports
Nov 12, 2024
Positron emission tomography (PET) utilizes radiotracers like [F]fluorodeoxyglucose (FDG) to measure brain activity in health and disease. Performing behavioral tasks between the FDG injection and the PET scan allows the FDG signal to reflect task-re...
MRI denoising with a non-blind deep complex-valued convolutional neural network.
NMR in biomedicine
Nov 11, 2024
MR images with high signal-to-noise ratio (SNR) provide more diagnostic information. Various methods for MRI denoising have been developed, but the majority of them operate on the magnitude image and neglect the phase information. Therefore, the goal...
Deep-learning models reveal how context and listener attention shape electrophysiological correlates of speech-to-language transformation.
PLoS computational biology
Nov 11, 2024
To transform continuous speech into words, the human brain must resolve variability across utterances in intonation, speech rate, volume, accents and so on. A promising approach to explaining this process has been to model electroencephalogram (EEG) ...
Unveiling the decision making process in Alzheimer's disease diagnosis: A case-based counterfactual methodology for explainable deep learning.
Journal of neuroscience methods
Nov 9, 2024
BACKGROUND: The field of Alzheimer's disease (AD) diagnosis is undergoing significant transformation due to the application of deep learning (DL) models. While DL surpasses traditional machine learning in disease prediction from structural magnetic r...
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