OBJECTIVE: Structural MRI (sMRI) increasingly offers insight into abnormalities inherent to schizophrenia. Previous machine learning applications suggest that individual classification is feasible and reliable and, however, is focused on the predicti...
Unbiased estimates of neuron numbers within substantia nigra are crucial for experimental Parkinson's disease models and gene-function studies. Unbiased stereological counting techniques with optical fractionation are successfully implemented, but ar...
Despite advances in bioinformatics, custom scripts remain a source of difficulty, slowing workflow development and hampering reproducibility. Here, we introduce Vectools, a command-line tool-suite to reduce reliance on custom scripts and improve repr...
PURPOSE: The confusion of MRI sequence names could be solved if MR images were automatically identified after image data acquisition. We revealed the ability of deep learning to classify head MRI sequences.
PURPOSE: We sought to assess whether machine learning-based classification approaches can improve the classification of pancreatic tumor models relative to more simplistic analysis methods, using T relaxation, CEST, and DCE MRI.
BACKGROUND: Based on international diagnostic guidelines, high-resolution CT plays a central part in the diagnosis of fibrotic lung disease. In the correct clinical context, when high-resolution CT appearances are those of usual interstitial pneumoni...
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Sep 14, 2018
BACKGROUND: Cardiovascular resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction ...
OBJECTIVE: Use of wearable ECG devices for arrhythmia screening is limited due to poor signal quality, small number of leads and short records, leading to incorrect recognition of pathological events. This paper introduces a novel approach to classif...
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Sep 13, 2018
In Alzheimer's research, Mild Cognitive Impairment (MCI) is an important intermediate stage between normal aging and Alzheimer's. How to distinguish MCI samples that finally convert to AD from those do not is an essential problem in the prevention an...
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Sep 13, 2018
Although alternations of brain functional networks (BFNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been considered as promising biomarkers for early Alzheimer's disease (AD) diagnosis, it is still challenging to...
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