AIMC Topic: Normal Distribution

Clear Filters Showing 161 to 170 of 282 articles

Artificial neural network metamodel for sensitivity analysis in a total hip replacement health economic model.

Expert review of pharmacoeconomics & outcomes research
: Metamodels have been used to approximate complex simulations and have many applications with sensitivity analysis, optimization, etc. However, their use in health economics is very limited. Application of artificial neural network (ANN) with a heal...

Gaussian synapses for probabilistic neural networks.

Nature communications
The recent decline in energy, size and complexity scaling of traditional von Neumann architecture has resurrected considerable interest in brain-inspired computing. Artificial neural networks (ANNs) based on emerging devices, such as memristors, achi...

Pixelwise Estimation of Signal-Dependent Image Noise Using Deep Residual Learning.

Computational intelligence and neuroscience
In traditional image denoising, noise level is an important scalar parameter which decides how much the input noisy image should be smoothed. Existing noise estimation methods often assume that the noise level is constant at every pixel. However, rea...

The effects of physics-based data augmentation on the generalizability of deep neural networks: Demonstration on nodule false-positive reduction.

Medical physics
PURPOSE: An important challenge for deep learning models is generalizing to new datasets that may be acquired with acquisition protocols different from the training set. It is not always feasible to expand training data to the range encountered in cl...

An investigation of quantitative accuracy for deep learning based denoising in oncological PET.

Physics in medicine and biology
Reducing radiation dose is important for PET imaging. However, reducing injection doses causes increased image noise and low signal-to-noise ratio (SNR), subsequently affecting diagnostic and quantitative accuracies. Deep learning methods have shown ...

Using an artificial neural network for fast mapping of the oxygen extraction fraction with combined QSM and quantitative BOLD.

Magnetic resonance in medicine
PURPOSE: To apply an artificial neural network (ANN) for fast and robust quantification of the oxygen extraction fraction (OEF) from a combined QSM and quantitative BOLD analysis of gradient echo data and to compare the ANN to a traditional quasi-New...

Compositionally-warped Gaussian processes.

Neural networks : the official journal of the International Neural Network Society
The Gaussian process (GP) is a nonparametric prior distribution over functions indexed by time, space, or other high-dimensional index set. The GP is a flexible model yet its limitation is given by its very nature: it can only model Gaussian marginal...

Deep learning-based selection of human sperm with high DNA integrity.

Communications biology
Despite the importance of sperm DNA to human reproduction, currently no method exists to assess individual sperm DNA quality prior to clinical selection. Traditionally, skilled clinicians select sperm based on a variety of morphological and motility ...

Scale-space approximated convolutional neural networks for retinal vessel segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Retinal fundus images are widely used to diagnose retinal diseases and can potentially be used for early diagnosis and prevention of chronic vascular diseases and diabetes. While various automatic retinal vessel segmentation...