AIMC Topic: Monte Carlo Method

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Enhancing Monte Carlo Tree Search for Retrosynthesis.

Journal of chemical information and modeling
Computer-Assisted Synthesis Programs are increasingly employed by organic chemists. Often, these tools combine neural networks for policy prediction with heuristic search algorithms. We propose two novel enhancements, which we call eUCT and dUCT, to ...

RAPTOR-AI: An open-source AI powered radiation protection toolkit for radioisotopes.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
Artificial intelligence (AI) has gained significant attention in various scientific fields due to its ability to process large datasets. In nuclear radiation physics, while AI presents exciting opportunities, it cannot replace physics-based models es...

Use of Machine Learning to Compare Disease Risk Scores and Propensity Scores Across Complex Confounding Scenarios: A Simulation Study.

Pharmacoepidemiology and drug safety
PURPOSE: The surge of treatments for COVID-19 in the second quarter of 2020 had a low prevalence of treatment and high outcome risk. Motivated by that, we conducted a simulation study comparing disease risk scores (DRS) and propensity scores (PS) usi...

Scatter and beam hardening effect corrections in pelvic region cone beam CT images using a convolutional neural network.

Radiological physics and technology
The aim of this study is to remove scattered photons and beam hardening effect in cone beam CT (CBCT) images and make an image available for treatment planning. To remove scattered photons and beam hardening effect, a convolutional neural network (CN...

Dose calculation in nuclear medicine with magnetic resonance imaging images using Monte Carlo method.

Radiation protection dosimetry
In recent years, scientists have been trying to convert magnetic resonance imaging (MRI) images into computed tomography (CT) images for dose calculations while taking advantage of the benefits of MRI images. The main approaches for image conversion ...

Uncertainty quantification for deep learning-based metastatic lesion segmentation on whole body PET/CT.

Physics in medicine and biology
Deep learning models are increasingly being implemented for automated medical image analysis to inform patient care. Most models, however, lack uncertainty information, without which the reliability of model outputs cannot be ensured. Several uncerta...

Improvement of deep learning-based dose conversion accuracy to a Monte Carlo algorithm in proton beam therapy for head and neck cancers.

Journal of radiation research
This study is aimed to clarify the effectiveness of the image-rotation technique and zooming augmentation to improve the accuracy of the deep learning (DL)-based dose conversion from pencil beam (PB) to Monte Carlo (MC) in proton beam therapy (PBT). ...

FasNet: a hybrid deep learning model with attention mechanisms and uncertainty estimation for liver tumor segmentation on LiTS17.

Scientific reports
Liver cancer, especially hepatocellular carcinoma (HCC), remains one of the most fatal cancers globally, emphasizing the critical need for accurate tumor segmentation to enable timely diagnosis and effective treatment planning. Traditional imaging te...

RadField3D: a data generator and data format for deep learning in radiation-protection dosimetry for medical applications.

Journal of radiological protection : official journal of the Society for Radiological Protection
In this research work, we present our open-source Geant4-based Monte-Carlo simulation application, called RadField3D, for generating three-dimensional radiation field datasets for dosimetry. Accompanying, we introduce a fast, machine-interpretable da...

Comparison of methods for tuning machine learning model hyper-parameters: with application to predicting high-need high-cost health care users.

BMC medical research methodology
BACKGROUND: Supervised machine learning is increasingly being used to estimate clinical predictive models. Several supervised machine learning models involve hyper-parameters, whose values must be judiciously specified to ensure adequate predictive p...