AIMC Topic: Humans
Showing 66101 to 66110 of 95995 articles
Liver Extraction Using Residual Convolution Neural Networks From Low-Dose CT Images.
IEEE transactions on bio-medical engineering
Jan 21, 2019
An efficient and precise liver extraction from computed tomography (CT) images is a crucial step for computer-aided hepatic diseases diagnosis and treatment. Considering the possible risk to patient's health due to X-ray radiation of repetitive CT ex...
Parallel imaging in time-of-flight magnetic resonance angiography using deep multistream convolutional neural networks.
Magnetic resonance in medicine
Jan 21, 2019
PURPOSE: To develop and evaluate a method of parallel imaging time-of-flight (TOF) MRA using deep multistream convolutional neural networks (CNNs).
Incorporation of a spectral model in a convolutional neural network for accelerated spectral fitting.
Magnetic resonance in medicine
Jan 21, 2019
PURPOSE: MRSI has shown great promise in the detection and monitoring of neurologic pathologies such as tumor. A necessary component of data processing includes the quantitation of each metabolite, typically done through fitting a model of the spectr...
Software
Algorithms
Databases, Factual
Brain Neoplasms
Humans
Neural Networks, Computer
Brain Mapping
Glioblastoma
Choline
Artifacts
Deep Learning
User-Computer Interface
Image Processing, Computer-Assisted
Models, Theoretical
Aspartic Acid
Signal-To-Noise Ratio
Computer Graphics
White Matter
Creatine
Magnetic Resonance Spectroscopy
Echo-Planar Imaging
Objective auditory brainstem response classification using machine learning.
International journal of audiology
Jan 21, 2019
OBJECTIVE: The objective of this study was to use machine learning in the form of a deep neural network to objectively classify paired auditory brainstem response waveforms into either: 'clear response', 'inconclusive' or 'response absent'.
(1)
Who is a better teacher for children with autism? Comparison of learning outcomes between robot-based and human-based interventions in gestural production and recognition.
Research in developmental disabilities
Jan 21, 2019
BACKGROUND: Individuals with autism spectrum disorder (ASD) tend to show deficits in engaging with humans. Previous findings have shown that robot-based training improves the gestural recognition and production of children with ASD. It is not known w...
Information flow reveals prediction limits in online social activity.
Nature human behaviour
Jan 21, 2019
Modern society depends on the flow of information over online social networks, and users of popular platforms generate substantial behavioural data about themselves and their social ties. However, it remains unclear what fundamental limits exist when...
Dermoscopy diagnosis of cancerous lesions utilizing dual deep learning algorithms via visual and audio (sonification) outputs: Laboratory and prospective observational studies.
EBioMedicine
Jan 20, 2019
BACKGROUND: Early diagnosis of skin cancer lesions by dermoscopy, the gold standard in dermatological imaging, calls for a diagnostic upscale. The aim of the study was to improve the accuracy of dermoscopic skin cancer diagnosis through use of novel ...
Predictors of in-hospital length of stay among cardiac patients: A machine learning approach.
International journal of cardiology
Jan 19, 2019
OBJECTIVE: The In-hospital length of stay (LOS) is expected to increase as cardiovascular diseases complexity increases and the population ages. This will affect healthcare systems especially with the current situation of decreased bed capacity and i...
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Network structure and input integration in competing firing rate models for decision-making.
Journal of computational neuroscience
Jan 19, 2019
Making a decision among numerous alternatives is a pervasive and central undertaking encountered by mammals in natural settings. While decision making for two-option tasks has been studied extensively both experimentally and theoretically, characteri...
Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals.
Computers in biology and medicine
Jan 19, 2019
Automated evaluation of a subject's neurocognitive performance (NCP) is a relevant topic in neurological and clinical studies. NCP represents the mental/cognitive human capacity in performing a specific task. It is difficult to develop the study prot...
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