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Monte Carlo Method

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Physics-driven learning of x-ray skin dose distribution in interventional procedures.

Medical physics
PURPOSE: Radiation doses accumulated during very complicated image-guided x-ray procedures have the potential to cause stochastic, but also deterministic effects, such as skin rashes or even hair loss. To monitor and reduce radiation-related risks to...

Denoising of Scintillation Camera Images Using a Deep Convolutional Neural Network: A Monte Carlo Simulation Approach.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Scintillation camera images contain a large amount of Poisson noise. We have investigated whether noise can be removed in whole-body bone scans using convolutional neural networks (CNNs) trained with sets of noisy and noiseless images obtained by Mon...

Decomposition feature selection with applications in detecting correlated biomarkers of bipolar disorders.

Statistics in medicine
Feature selection is an important initial step of exploratory analysis in biomedical studies. Its main objective is to eliminate the covariates that are uncorrelated with the outcome. For highly correlated covariates, traditional feature selection me...

Estimation of the radiation dose in pregnancy: an automated patient-specific model using convolutional neural networks.

European radiology
OBJECTIVES: The conceptus dose during diagnostic imaging procedures for pregnant patients raises health concerns owing to the high radiosensitivity of the developing embryo/fetus. The aim of this work is to develop a methodology for automated constru...

Projection-domain scatter correction for cone beam computed tomography using a residual convolutional neural network.

Medical physics
PURPOSE: Scatter is a major factor degrading the image quality of cone beam computed tomography (CBCT). Conventional scatter correction strategies require handcrafted analytical models with ad hoc assumptions, which often leads to less accurate scatt...

Identification of leukemia stem cell expression signatures through Monte Carlo feature selection strategy and support vector machine.

Cancer gene therapy
Acute myeloid leukemia (AML) is a type of blood cancer characterized by the rapid growth of immature white blood cells from the bone marrow. Therapy resistance resulting from the persistence of leukemia stem cells (LSCs) are found in numerous patient...

Physically informed artificial neural networks for atomistic modeling of materials.

Nature communications
Large-scale atomistic computer simulations of materials heavily rely on interatomic potentials predicting the energy and Newtonian forces on atoms. Traditional interatomic potentials are based on physical intuition but contain few adjustable paramete...

Comprehensive Eutrophication Assessment Based on Fuzzy Matter Element Model and Monte Carlo-Triangular Fuzzy Numbers Approach.

International journal of environmental research and public health
Evaluating the eutrophication level of lakes with a single method alone is challenging since uncertain, fuzzy, and complex processes exist in eutrophication evaluations. The parameters selected for assessing eutrophication include chlorophyII-a, chem...

Identifying psychosis spectrum disorder from experience sampling data using machine learning approaches.

Schizophrenia research
The ubiquity of smartphones opened up the possibility of widespread use of the Experience Sampling Method (ESM). The method is used to collect longitudinal data of participants' daily life experiences and is ideal to capture fluctuations in emotions ...

A comparison of machine learning algorithms and covariate balance measures for propensity score matching and weighting.

Biometrical journal. Biometrische Zeitschrift
Propensity score matching (PSM) and propensity score weighting (PSW) are popular tools to estimate causal effects in observational studies. We address two open issues: how to estimate propensity scores and assess covariate balance. Using simulations,...