AIMC Topic: Neural Networks, Computer

Clear Filters Showing 18351 to 18360 of 31376 articles

Predicting the central 10 degrees visual field in glaucoma by applying a deep learning algorithm to optical coherence tomography images.

Scientific reports
We aimed to develop a model to predict visual field (VF) in the central 10 degrees in patients with glaucoma, by training a convolutional neural network (CNN) with optical coherence tomography (OCT) images and adjusting the values with Humphrey Field...

Denoising non-steady state dynamic PET data using a feed-forward neural network.

Physics in medicine and biology
The quality of reconstructed dynamic PET images, as well as the statistical reliability of the estimated pharmacokinetic parameters is often compromised by high levels of statistical noise, particularly at the voxel level. Many denoising strategies h...

Multi-task edge-recalibrated network for male pelvic multi-organ segmentation on CT images.

Physics in medicine and biology
Automated male pelvic multi-organ segmentation on CT images is highly desired for applications, including radiotherapy planning. To further improve the performance and efficiency of existing automated segmentation methods, in this study, we propose a...

Hypergraph Neural Network for Skeleton-Based Action Recognition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Recently, skeleton-based human action recognition has attracted a lot of research attention in the field of computer vision. Graph convolutional networks (GCNs), which model the human body skeletons as spatial-temporal graphs, have shown excellent re...

A cumulative-risk assessment method based on an artificial neural network model for the water environment.

Environmental science and pollution research international
To analyze the cumulative risks to the water environment, the backpropagation artificial neural network (BP-ANN), a self-adapting algorithm, was proposed in this study. A new comprehensive indicator of cumulative risks was formed by combining the wat...

Integrated functional neuronal network analysis of 3D silk-collagen scaffold-based mouse cortical culture.

STAR protocols
Bioengineered 3D tunable neuronal constructs are a versatile platform for studying neuronal network functions, offering numerous advantages over existing technologies and providing for the discovery of new biological insights. Functional neural netwo...

Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients.

Nature cancer
Cell-line screens create expansive datasets for learning predictive markers of drug response, but these models do not readily translate to the clinic with its diverse contexts and limited data. In the present study, we apply a recently developed tech...

Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification.

Medical & biological engineering & computing
Deep learning (DL) has been successfully applied to the diagnosis of ophthalmic diseases. However, rare diseases are commonly neglected due to insufficient data. Here, we demonstrate that few-shot learning (FSL) using a generative adversarial network...

Deep Convolutional and LSTM Networks on Multi-Channel Time Series Data for Gait Phase Recognition.

Sensors (Basel, Switzerland)
With an ageing society comes the increased prevalence of gait disorders. The restriction of mobility leads to a considerable reduction in the quality of life, because associated falls increase morbidity and mortality. Consideration of gait analysis d...

Exploring convolutional neural networks and spatial video for on-the-ground mapping in informal settlements.

International journal of health geographics
BACKGROUND: The health burden in developing world informal settlements often coincides with a lack of spatial data that could be used to guide intervention strategies. Spatial video (SV) has proven to be a useful tool to collect environmental and soc...