AIMC Topic: Neural Networks, Computer

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Brain tumor segmentation using holistically nested neural networks in MRI images.

Medical physics
PURPOSE: Gliomas are rapidly progressive, neurologically devastating, largely fatal brain tumors. Magnetic resonance imaging (MRI) is a widely used technique employed in the diagnosis and management of gliomas in clinical practice. MRI is also the st...

Learning representations for the early detection of sepsis with deep neural networks.

Computers in biology and medicine
BACKGROUND: Sepsis is one of the leading causes of death in intensive care unit patients. Early detection of sepsis is vital because mortality increases as the sepsis stage worsens.

New insights into olivo-cerebellar circuits for learning from a small training sample.

Current opinion in neurobiology
Artificial intelligence such as deep neural networks exhibited remarkable performance in simulated video games and 'Go'. In contrast, most humanoid robots in the DARPA Robotics Challenge fell down to ground. The dramatic contrast in performance is ma...

Automatic detection of hemorrhagic pericardial effusion on PMCT using deep learning - a feasibility study.

Forensic science, medicine, and pathology
Post mortem computed tomography (PMCT) can be used as a triage tool to better identify cases with a possibly non-natural cause of death, especially when high caseloads make it impossible to perform autopsies on all cases. Substantial data can be gene...

A New Hybrid Model Using an Autoregressive Integrated Moving Average and a Generalized Regression Neural Network for the Incidence of Tuberculosis in Heng County, China.

The American journal of tropical medicine and hygiene
It is a daunting task to eradicate tuberculosis completely in Heng County due to a large transient population, human immunodeficiency virus/tuberculosis coinfection, and latent infection. Thus, a high-precision forecasting model can be used for the p...

Scene text detection via extremal region based double threshold convolutional network classification.

PloS one
In this paper, we present a robust text detection approach in natural images which is based on region proposal mechanism. A powerful low-level detector named saliency enhanced-MSER extended from the widely-used MSER is proposed by incorporating salie...

Spiking Neural P Systems with Communication on Request.

International journal of neural systems
Spiking Neural [Formula: see text] Systems are Neural System models characterized by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural [Formula: see text] systems invest...

Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks.

Methods of information in medicine
OBJECTIVE: To describe and compare logistic regression and neural network modeling strategies to predict hospital admission or transfer following initial presentation to Emergency Department (ED) triage with and without the addition of natural langua...

A neural network multi-task learning approach to biomedical named entity recognition.

BMC bioinformatics
BACKGROUND: Named Entity Recognition (NER) is a key task in biomedical text mining. Accurate NER systems require task-specific, manually-annotated datasets, which are expensive to develop and thus limited in size. Since such datasets contain related ...

Improving fold resistance prediction of HIV-1 against protease and reverse transcriptase inhibitors using artificial neural networks.

BMC bioinformatics
BACKGROUND: Drug resistance in HIV treatment is still a worldwide problem. Predicting resistance to antiretrovirals (ARVs) before starting any treatment is important. Prediction accuracy is essential, as low-accuracy predictions increase the risk of ...