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Pengfei Zhao and Ze Liu
The three-dimensional (3D) reconstruction of Electromagnetic Tomography (EMT) is an important task for many applications, such as the non-destructive testing of inner defects in rail systems. Additionally, image reconstruction algorithms utilizing deep l...
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Abdul Rahaman Wahab Sait and Ali Mohammad Alorsan Bani Awad
Coronary artery disease (CAD) is the most prevalent form of cardiovascular disease that may result in myocardial infarction. Annually, it leads to millions of fatalities and causes billions of dollars in global economic losses. Limited resources and comp...
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Jiayi Peng, Zhenzhong Shen, Wenbing Zhang and Wen Song
Permeability characteristics in coarse-grained soil is pivotal for enhancing the understanding of its seepage behavior and effectively managing it, directly impacting the design, construction, and operational safety of embankment dams. Furthermore, these...
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Abdul Rahaman Wahab Sait
Globally, lung cancer (LC) is the primary factor for the highest cancer-related mortality rate. Deep learning (DL)-based medical image analysis plays a crucial role in LC detection and diagnosis. It can identify early signs of LC using positron emission ...
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José V. Gaspareto, Jocenei A. T. de Oliveira, Everton Andrade and Luiz F. Pires
Representative elementary volume (REV) is required for representative measurements of soil physical properties. However, questions may arise whether REV depends on how the soil structure is modified or whether processes in the soil affect REV. Here, we e...
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Thomas P. Oghalai, Ryan Long, Wihan Kim, Brian E. Applegate and John S. Oghalai
Optical Coherence Tomography (OCT) is a light-based imaging modality that is used widely in the diagnosis and management of eye disease, and it is starting to become used to evaluate for ear disease. However, manual image analysis to interpret the anatom...
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Takeshi Kojima and Tetsuya Yoshinaga
Recently, an extended family of power-divergence measures with two parameters was proposed together with an iterative reconstruction algorithm based on minimization of the divergence measure as an objective function of the reconstructed images for comput...
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Chowdhury Abrar Faiyaz, Pabel Shahrear, Rakibul Alam Shamim, Thilo Strauss and Taufiquar Khan
This paper aims to determine whether regularization improves image reconstruction in electrical impedance tomography (EIT) using a radial basis network. The primary purpose is to
investigate the effect of regularization to estimate the network parameters...
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Elena Loli Piccolomini, Marco Prato, Margherita Scipione and Andrea Sebastiani
In this paper, we propose a new deep learning approach based on unfolded neural networks for the reconstruction of X-ray computed tomography images from few views. We start from a model-based approach in a compressed sensing framework, described by the m...
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Ferenc Izsák and Taki Eddine Djebbar
We propose neural-network-based algorithms for the numerical solution of boundary-value problems for the Laplace equation. Such a numerical solution is inherently mesh-free, and in the approximation process, stochastic algorithms are employed. The chief ...
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