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Monte Carlo Method

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Optimizing immune cell therapies with artificial intelligence.

Journal of theoretical biology
PURPOSE: We determine an optimal injection pattern for anti-vascular endothelial growth factor (VEGF) and for the combination of anti-VEGF and unlicensed dendritic cells.

Learning SPECT detector angular response function with neural network for accelerating Monte-Carlo simulations.

Physics in medicine and biology
A method to speed up [Formula: see text] simulations of single photon emission computed tomography (SPECT) imaging is proposed. It uses an artificial neural network (ANN) to learn the angular response function (ARF) of a collimator-detector system. T...

Perturbation Theory-Machine Learning Study of Zeolite Materials Desilication.

Journal of chemical information and modeling
Zeolites are important materials for research and industrial applications. Mesopores are often introduced by desilication but other properties are also affected, making its optimization difficult. In this work, we demonstrate that Perturbation Theory...

Unsupervised classification of tissues composition for Monte Carlo dose calculation.

Physics in medicine and biology
The purpose of this study is to investigate the potential of k-means clustering to efficiently reduce the variety of materials needed in Monte Carlo (MC) dose calculation. A numerical phantom with 31 human tissues surrounded by water is created. K-me...

A new mechanical approach to handle generalized Hopfield neural networks.

Neural networks : the official journal of the International Neural Network Society
We propose a modification of the cost function of the Hopfield model whose salient features shine in its Taylor expansion and result in more than pairwise interactions with alternate signs, suggesting a unified framework for handling both with deep l...

Optimal dynamic regimens with artificial intelligence: The case of temozolomide.

PloS one
We determine an optimal protocol for temozolomide using population variability and dynamic optimization techniques inspired by artificial intelligence. We use a Pharmacokinetics/Pharmacodynamics (PK/PD) model based on Faivre and coauthors (Faivre, et...

Spatial extreme learning machines: An application on prediction of disease counts.

Statistical methods in medical research
Extreme learning machines have gained a lot of attention by the machine learning community because of its interesting properties and computational advantages. With the increase in collection of information nowadays, many sources of data have missing ...

Nonnegative Matrix Factorization for identification of unknown number of sources emitting delayed signals.

PloS one
Factor analysis is broadly used as a powerful unsupervised machine learning tool for reconstruction of hidden features in recorded mixtures of signals. In the case of a linear approximation, the mixtures can be decomposed by a variety of model-free B...

Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features.

Journal of the American College of Radiology : JACR
PURPOSE: The aim of this study was to determine whether deep features extracted from digital mammograms using a pretrained deep convolutional neural network are prognostic of occult invasive disease for patients with ductal carcinoma in situ (DCIS) o...