AIMC Topic: Neural Networks, Computer

Clear Filters Showing 23031 to 23040 of 31376 articles

Application of Bioactivity Profile-Based Fingerprints for Building Machine Learning Models.

Journal of chemical information and modeling
The volume of high throughput screening data has considerably increased since the beginning of the automated biochemical and cell-based assays era. This information-rich data source provides tremendous repurposing opportunities for data mining. It wa...

QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy.

NeuroImage
Whole brain segmentation from structural magnetic resonance imaging (MRI) is a prerequisite for most morphological analyses, but is computationally intense and can therefore delay the availability of image markers after scan acquisition. We introduce...

A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography.

IEEE transactions on medical imaging
Various types of atherosclerotic plaque and varying grades of stenosis could lead to different management of patients with a coronary artery disease. Therefore, it is crucial to detect and classify the type of coronary artery plaque, as well as to de...

The Complex Behaviour of a Simple Neural Oscillator Model in the Human Cortex.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The brain is a complex organ responsible for memory storage and reasoning; however, the mechanisms underlying these processes remain unknown. This paper forms a contribution to a lot of theoretical studies devoted to regular or chaotic oscillations o...

Automatic tumor segmentation in breast ultrasound images using a dilated fully convolutional network combined with an active contour model.

Medical physics
PURPOSE: Due to the low contrast, blurry boundaries, and large amount of shadows in breast ultrasound (BUS) images, automatic tumor segmentation remains a challenging task. Deep learning provides a solution to this problem, since it can effectively e...

A deep learning approach to automatic detection of early glaucoma from visual fields.

PloS one
PURPOSE: To investigate the suitability of multi-scale spatial information in 30o visual fields (VF), computed from a Convolutional Neural Network (CNN) classifier, for early-glaucoma vs. control discrimination.

Convolutional neural network evaluation of over-scanning in lung computed tomography.

Diagnostic and interventional imaging
INTRODUCTION: The purpose of this study was to develop a convolutional neural network (CNN) to determine the extent of over-scanning in the Z-direction associated with lung computed tomography (CT) examinations.

Time-lagged effects of weekly climatic and socio-economic factors on ANN municipal yard waste prediction models.

Waste management (New York, N.Y.)
Efficient and effective solid waste management requires sufficient ability to predict the operational capacity of a system correctly. Waste prediction models have been widely studied and these models are always being challenged to perform more accura...

A density-based competitive data stream clustering network with self-adaptive distance metric.

Neural networks : the official journal of the International Neural Network Society
Data stream clustering is a branch of clustering where patterns are processed as an ordered sequence. In this paper, we propose an unsupervised learning neural network named Density Based Self Organizing Incremental Neural Network(DenSOINN) for data ...

Synchronization-induced spike termination in networks of bistable neurons.

Neural networks : the official journal of the International Neural Network Society
We observe and study a self-organized phenomenon whereby the activity in a network of spiking neurons spontaneously terminates. We consider different types of populations, consisting of bistable model neurons connected electrically by gap junctions, ...