AIMC Topic: Neural Networks, Computer

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Learning through ferroelectric domain dynamics in solid-state synapses.

Nature communications
In the brain, learning is achieved through the ability of synapses to reconfigure the strength by which they connect neurons (synaptic plasticity). In promising solid-state synapses called memristors, conductance can be finely tuned by voltage pulses...

A Novel Fusion Framework Based on Adaptive PCNN in NSCT Domain for Whole-Body PET and CT Images.

Computational and mathematical methods in medicine
The PET and CT fusion images, combining the anatomical and functional information, have important clinical meaning. This paper proposes a novel fusion framework based on adaptive pulse-coupled neural networks (PCNNs) in nonsubsampled contourlet trans...

Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer.

Computational and mathematical methods in medicine
Medical imaging techniques have widely been in use in the diagnosis and detection of breast cancer. The drawback of applying these techniques is the large time consumption in the manual diagnosis of each image pattern by a professional radiologist. A...

Adaptive Neural Tracking Control for Switched High-Order Stochastic Nonlinear Systems.

IEEE transactions on cybernetics
This paper deals with adaptive neural tracking control design for a class of switched high-order stochastic nonlinear systems with unknown uncertainties and arbitrary deterministic switching. The considered issues are: 1) completely unknown uncertain...

A neural joint model for entity and relation extraction from biomedical text.

BMC bioinformatics
BACKGROUND: Extracting biomedical entities and their relations from text has important applications on biomedical research. Previous work primarily utilized feature-based pipeline models to process this task. Many efforts need to be made on feature e...

ARACHNE: A neural-neuroglial network builder with remotely controlled parallel computing.

PLoS computational biology
Creating and running realistic models of neural networks has hitherto been a task for computing professionals rather than experimental neuroscientists. This is mainly because such networks usually engage substantial computational resources, the handl...

SLIDE: automatic spine level identification system using a deep convolutional neural network.

International journal of computer assisted radiology and surgery
PURPOSE: Percutaneous spinal needle insertion procedures often require proper identification of the vertebral level to effectively and safely deliver analgesic agents. The current clinical method involves "blind" identification of the vertebral level...

Ultrasound Standard Plane Detection Using a Composite Neural Network Framework.

IEEE transactions on cybernetics
Ultrasound (US) imaging is a widely used screening tool for obstetric examination and diagnosis. Accurate acquisition of fetal standard planes with key anatomical structures is very crucial for substantial biometric measurement and diagnosis. However...

Addressing multi-label imbalance problem of surgical tool detection using CNN.

International journal of computer assisted radiology and surgery
PURPOSE: A fully automated surgical tool detection framework is proposed for endoscopic video streams. State-of-the-art surgical tool detection methods rely on supervised one-vs-all or multi-class classification techniques, completely ignoring the co...

Application of artificial neural network in medical geochemistry.

Environmental geochemistry and health
For the evaluation of various adverse health effects of chemical elements occurring in the environment on humans, the comparison and linking of geochemical data (chemical composition of groundwater, soils, and dusts) with data on health status of pop...