Simulation of biomedical signals and images using Monte Carlo methods for training of deep learning networks

Mavaddat, Navid; Ahderom, Selam; Tiporlini, Valentina; Alameh, Kamal

doi:10.1016/b978-0-12-819061-6.00009-4

Cited by 2 publications

(2 citation statements)

References 29 publications

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“…Traditional approaches such as the Monte Carlo simulation (MCS) method with the help of a nondestructive examination system are used to predict the probability of failure of the pipelines. The MCS is based on the concept of numeric sampling assisting in developing probabilistic models [ 17 ]. In essence, the MCS generates a great number of cases and criteria value conversions for each case [ 18 ].…”

Section: Conventional Residual Strength Assessment Methodsmentioning

confidence: 99%

A Review of Finite Element Analysis and Artificial Neural Networks as Failure Pressure Prediction Tools for Corroded Pipelines

Kumar

Arumugam

et al. 2021

Materials

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This paper discusses the capabilities of artificial neural networks (ANNs) when integrated with the finite element method (FEM) and utilized as prediction tools to predict the failure pressure of corroded pipelines. The use of conventional residual strength assessment methods has proven to produce predictions that are conservative, and this, in turn, costs companies by leading to premature maintenance and replacement. ANNs and FEM have proven to be strong failure pressure prediction tools, and they are being utilized to replace the time-consuming methods and conventional codes. FEM is widely used to evaluate the structural integrity of corroded pipelines, and the integration of ANNs into this process greatly reduces the time taken to obtain accurate results.

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Section: Conventional Residual Strength Assessment Methodsmentioning

confidence: 99%

A Review of Finite Element Analysis and Artificial Neural Networks as Failure Pressure Prediction Tools for Corroded Pipelines

Kumar

Arumugam

et al. 2021

Materials

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“…The MC method has many improvements that incorporate other interaction types such as fluorescence and Raman scattering [25], time and frequency-resolved setups, and extensions to 3D samples [26][27][28]. More recently, MC methods have also found applications in the food industry [29], deep learning [30], to study chemical processes [31], and mainly in biomedicine. Many available Monte-Carlo-based tools are online [11,31,32], customized for light propagation in biological tissues to help the biomedical community access efficient and accurate modeling of light transport.…”

Section: Photon Propagation Through Tissuementioning

confidence: 99%

Tutorial on the Use of Deep Learning in Diffuse Optical Tomography

et al. 2022

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Diffuse optical tomography using deep learning is an emerging technology that has found impressive medical diagnostic applications. However, creating an optical imaging system that uses visible and near-infrared (NIR) light is not straightforward due to photon absorption and multi-scattering by tissues. The high distortion levels caused due to these effects make the image reconstruction incredibly challenging. To overcome these challenges, various techniques have been proposed in the past, with varying success. One of the most successful techniques is the application of deep learning algorithms in diffuse optical tomography. This article discusses the current state-of-the-art diffuse optical tomography systems and comprehensively reviews the deep learning algorithms used in image reconstruction. This article attempts to provide researchers with the necessary background and tools to implement deep learning methods to solve diffuse optical tomography.

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Simulation of biomedical signals and images using Monte Carlo methods for training of deep learning networks

Cited by 2 publications

References 29 publications

A Review of Finite Element Analysis and Artificial Neural Networks as Failure Pressure Prediction Tools for Corroded Pipelines

A Review of Finite Element Analysis and Artificial Neural Networks as Failure Pressure Prediction Tools for Corroded Pipelines

Tutorial on the Use of Deep Learning in Diffuse Optical Tomography

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