AIMC Topic: Neural Networks, Computer

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Automatic real-time gait event detection in children using deep neural networks.

PloS one
Annotation of foot-contact and foot-off events is the initial step in post-processing for most quantitative gait analysis workflows. If clean force plate strikes are present, the events can be automatically detected. Otherwise, annotation of gait eve...

Water-fat separation and parameter mapping in cardiac MRI via deep learning with a convolutional neural network.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Water-fat separation is a postprocessing technique most commonly applied to multiple-gradient-echo magnetic resonance (MR) images to identify fat, provide images with fat suppression, and to measure fat tissue concentration. Recently, Num...

A survey of neural network-based cancer prediction models from microarray data.

Artificial intelligence in medicine
Neural networks are powerful tools used widely for building cancer prediction models from microarray data. We review the most recently proposed models to highlight the roles of neural networks in predicting cancer from gene expression data. We identi...

Automatic Disease Annotation From Radiology Reports Using Artificial Intelligence Implemented by a Recurrent Neural Network.

AJR. American journal of roentgenology
OBJECTIVE: Radiology reports are rich resources for biomedical researchers. Before utilization of radiology reports, experts must manually review these reports to identify the categories. In fact, automatically categorizing electronic medical record ...

Deep learning for cell image segmentation and ranking.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Ninety years after its invention, the Pap test continues to be the most used method for the early identification of cervical precancerous lesions. In this test, the cytopathologists look for microscopic abnormalities in and around the cells, which is...

Deep learning for molecular generation.

Future medicinal chemistry
De novo drug design aims to generate novel chemical compounds with desirable chemical and pharmacological properties from scratch using computer-based methods. Recently, deep generative neural networks have become a very active research frontier in d...

An ecologically constrained procedure for sensitivity analysis of Artificial Neural Networks and other empirical models.

PloS one
Sensitivity analysis applied to Artificial Neural Networks (ANNs) as well as to other types of empirical ecological models allows assessing the importance of environmental predictive variables in affecting species distribution or other target variabl...

Prediction of mortality following pediatric heart transplant using machine learning algorithms.

Pediatric transplantation
BACKGROUND: Optimizing transplant candidates' priority for donor organs depends on the accurate assessment of post-transplant outcomes. Due to the complexity of transplantation and the wide range of possible serious complications, recipient outcomes ...

Disturbance and uncertainty rejection performance for fractional-order complex dynamical networks.

Neural networks : the official journal of the International Neural Network Society
This paper investigates the synchronization issue for a family of time-delayed fractional-order complex dynamical networks (FCDNs) with time delay, unknown bounded uncertainty and disturbance. A novel fractional uncertainty and disturbance estimator ...