AIMC Topic: Neural Networks, Computer

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EEG-based mild depression recognition using convolutional neural network.

Medical & biological engineering & computing
Electroencephalography (EEG)-based studies focus on depression recognition using data mining methods, while those on mild depression are yet in infancy, especially in effective monitoring and quantitative measure aspects. Aiming at mild depression re...

Leaving hip rotation out of a conventional 3D gait model improves discrimination of pathological gait in cerebral palsy: A novel neural network analysis.

Gait & posture
BACKGROUND: Complex clinical gait analysis results can be expressed as single number gait deviations by applying multivariate processing methods. The original Movement Deviation Profile (MDP) quantifies the deviation of abnormal gait using the most t...

Analyzing biological and artificial neural networks: challenges with opportunities for synergy?

Current opinion in neurobiology
Deep neural networks (DNNs) transform stimuli across multiple processing stages to produce representations that can be used to solve complex tasks, such as object recognition in images. However, a full understanding of how they achieve this remains e...

Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation.

Medical physics
PURPOSE: Reliable automated segmentation of the prostate is indispensable for image-guided prostate interventions. However, the segmentation task is challenging due to inhomogeneous intensity distributions, variation in prostate anatomy, among other ...

A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals.

International journal of environmental research and public health
Sleep disorder is a symptom of many neurological diseases that may significantly affect the quality of daily life. Traditional methods are time-consuming and involve the manual scoring of polysomnogram (PSG) signals obtained in a laboratory environme...

Cobb Angle Measurement of Spine from X-Ray Images Using Convolutional Neural Network.

Computational and mathematical methods in medicine
Scoliosis is a common spinal condition where the spine curves to the side and thus deforms the spine. Curvature estimation provides a powerful index to evaluate the deformation severity of scoliosis. In current clinical diagnosis, the standard curvat...

Applications of artificial neural networks in health care organizational decision-making: A scoping review.

PloS one
Health care organizations are leveraging machine-learning techniques, such as artificial neural networks (ANN), to improve delivery of care at a reduced cost. Applications of ANN to diagnosis are well-known; however, ANN are increasingly used to info...

Patch-Based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation.

IEEE transactions on medical imaging
Glaucoma is a leading cause of irreversible blindness. Accurate segmentation of the optic disc (OD) and optic cup (OC) from fundus images is beneficial to glaucoma screening and diagnosis. Recently, convolutional neural networks demonstrate promising...

Medical image classification using synergic deep learning.

Medical image analysis
The classification of medical images is an essential task in computer-aided diagnosis, medical image retrieval and mining. Although deep learning has shown proven advantages over traditional methods that rely on the handcrafted features, it remains c...

How the input shapes the acquisition of verb morphology: Elicited production and computational modelling in two highly inflected languages.

Cognitive psychology
The aim of the present work was to develop a computational model of how children acquire inflectional morphology for marking person and number; one of the central challenges in language development. First, in order to establish which putative learnin...