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Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans

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Journal of Healthcare Engineering 2022年第1期2022卷 3861161页

作者： Alzu'Bi, Dalia Abdullah, Malak Hmeidi, Ismail Alazab, Rami Gharaibeh, Maha El-Heis, Mwaffaq Almotairi, Khaled H. Forestiero, Agostino Hussein, Ahmad Mohdaziz Abualigah, Laith Department of Computer Information Systems Jordan University of Science and Technology Irbid2210 Jordan Department of General Surgery and Urology University of Science and Technology Irbid22110 Jordan Department of Diagnostic and Interventional Radiology Faculty of Medicine Jordan University of Science and Technology Irbid2210 Jordan Computer Engineering Department Computer and Information Systems College Umm Al-Qura University Makkah21955 Saudi Arabia Institute for High Performance Computing and Networking CNR CS Rende Italy Deanship of E-Learning and Distance Education Umm Al-Qura University Makkah21955 Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Faculty of Information Technology Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most common tumor in both men and women worldwide. The early detection of KT has significant benefits in reducing death rates, producing preventive measures that reduce effects, and overcoming the tumor. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of deep learning (DL) can save diagnosis time, improve test accuracy, reduce costs, and reduce the radiologist's workload. In this paper, we present detection models for diagnosing the presence of KTs in computed tomography (CT) scans. Toward detecting and classifying KT, we proposed 2D-CNN models;three models are concerning KT detection such as a 2D convolutional neural network with six layers (CNN-6), a ResNet50 with 50 layers, and a VGG16 with 16 layers. The last model is for KT classification as a 2D convolutional neural network with four layers (CNN-4). In addition, a novel dataset from the King Abdullah University Hospital (KAUH) has been collected that consists of 8,400 images of 120 adult patients who have performed CT scans for suspected kidney masses. The dataset was divided into 80% for the training set and 20% for the testing set. The accuracy results for the detection models of 2D CNN-6 and ResNet50 reached 97%, 96%, and 60%, respectively. At the same time, the accuracy results for the classification model of the 2D CNN-4 reached 92%. Our novel models achieved promising results;they enhance the diagnosis of patient conditions with high accuracy, reducing radiologist's workload and providing them with a tool that can automatically assess the condition of the kidneys, reducing the risk of misdiagnosis. Furthermore, increasing the quality of healthcare service and early detection can change the disease's track and preserve the patient's life. © 2022 Dalia Alzu'bi et al.

关键词： Convolutional neural networks

Determining HEDP Foams' Quality with Multi-View Deep Learning Classification

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Determining HEDP Foams' Quality with Multi-View Deep Learnin...

Artificial Intelligence and Machine Learning for scientific Applications (AI4S), IEEE/ACM International Workshop on

作者： Nadav Schneider Matan Rusanovsky Raz Gvishi Gal Oren Technology Division Soreq Nuclear Research Center Yavne Israel Israel Atomic Energy Commission Scientific Computing Center Nuclear Research Center Negev Israel Department of Physics Nuclear Research Center Negev Israel Applied Physics Division Soreq Nuclear Research Center ─ Yavne Israel Department of Computer Science Technion Israel Institute of Technology Israel

ISBN: (纸本)9781665462082

High energy density physics (HEDP) experiments commonly involve a dynamic wave-front propagating inside a lowdensity foam. This effect affects its density and hence, its transparency. A common problem in foam production is the creation of defective foams. Accurate information on their dimension and homogeneity is required to classify the foams' quality. Therefore, those parameters are being characterized using a 3D-measuring laser confocal microscope. For each foam, five images are taken: two 2D images representing the top and bottom surface foam planes and three images of side cross-sections from 3D scannings. An expert has to do the complicated, harsh, and exhausting work of manually classifying the foam's quality through the image set and only then determine whether the foam can be used in experiments or not. Currently, quality has two binary levels of normal vs. defective. At the same time, experts are commonly required to classify a sub-class of normal-defective, i.e., defective foams but might be sufficient for the needed experiment. This sub-class is problematic due to inconclusive judgment that is primarily intuitive. In this work, we present a novel state─of─the─art multi-view deep learning classification model that mimics the physicist's perspective by automatically determining the foams' quality classification and thus aids the expert. Our model achieved 86% accuracy on upper and lower surface foam planes and 82% on the entire set, suggesting interesting heuristics to the problem. A significant added value in this work is the ability to regress the foam quality instead of binary deduction and even explain the decision visually. The source code used in this work, as well as other relevant sources, are available at: https://***/***.

关键词： Deep learning Three-dimensional displays Source coding Microscopy Conferences MIMICs Lasers

Morphology of proliferating epithelial cellular tissue

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arXiv 2018年

作者： Madhikar, Pranav Åström, Jan Baumeier, Björn Karttunen, Mikko Department of Mathematics and Computer Science Institute for Complex Molecular Systems Eindhoven University of Technology Eindhoven5600 MB Netherlands CSC Scientific Computing Ltd Kägelstranden 14 Esbo02150 Finland Department of Chemistry Department of Applied Mathematics Western University 1151 Richmond Street LondonONN6A 5B7 Canada

We investigate morphologies of proliferating cellular tissue using a newly developed numerical simulation model for mechanical cell division. The model reproduces structures of simple multi-cellular organisms via simple rules for selective division and division plane orientation. The model is applied to a bimodal mixture of stiff cells with a low growth potential and soft cells with a high growth potential. In an even mixture, the soft cells develop into a tissue matrix and the stiff cells into a dendrite-like network structure. For soft cell inclusion in a stiff cellular matrix, the soft cells develop to a fast growing tumour like structure that gradually evacuates the stiff cell matrix. With increasing inter-cell friction, the tumour growth slows down and parts of it is driven to self-inflicted cell death. Copyright © 2018, The Authors. All rights reserved.

关键词： Mixtures

Characteristics Fluctuation of Sub-3-nm Bulk FinFET Devices Induced by Random Interface Traps

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Characteristics Fluctuation of Sub-3-nm Bulk FinFET Devices ...

IEEE Conference on Nanotechnology

作者： Sekhar Reddy Kola Min-Hui Chuang Yiming Li Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering Institute of Communications Engineering Institute of Biomedical Engineering Institute of Pioneer Semiconductor Innovation Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Hsinchu Taiwan

Three-dimensional (3D) bulk fin-typed field effect transistors (FinFETs) have emerged as key devices that can scale down the technology node beyond 22-nm. However, the scaled devices have created new sources of fluctuation inherent in 3D geometry. The interface trap is one such fluctuation that is caused by the trapping and de-trapping of charge carriers and has an adverse effect on device characteristics and variability. In this work, we study impacts of random interface traps (RITs) on electrical characteristic of bulk FinFETs by using a 3D quantum-mechanically corrected device simulation. RIT position effects on short channel effects (SCEs) are examined with physical governed influence to show the major fluctuations. More than 50% reductions of the RITs-induced characteristic fluctuation of the germanium (Ge) devices are observed, compared with Si devices. The Ge ones can reduce SCE variations and exhibit high immunity to RITs.

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Integrated Construction of Multimodal Atlases with Structural Connectomes in the Space of Riemannian Metrics

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arXiv 2021年

作者： Campbell, Kristen M. Dai, Haocheng Su, Zhe Bauer, Martin Fletcher, P. Thomas Joshi, Sarang C. Scientific Computing and Imaging Institute University of Utah Salt Lake CityUT United States School of Computing University of Utah Salt Lake CityUT United States Department of Neurology University of California Los Angeles Los AngelesCA United States Department of Mathematics Florida State University TallahasseeFL United States Department of Electrical & Computer Engineering University of Virginia CharlottesvilleVA United States Department of Computer Science University of Virginia CharlottesvilleVA United States Department of Bioengineering University of Utah Salt Lake CityUT United States

The structural network of the brain, or structural connectome, can be represented by fiber bundles generated by a variety of tractography methods. While such methods give qualitative insights into brain structure, there is controversy over whether they can provide quantitative information, especially at the population level. In order to enable population-level statistical analysis of the structural connectome, we propose representing a connectome as a Riemannian metric, which is a point on an infinite-dimensional manifold. We equip this manifold with the Ebin metric, a natural metric structure for this space, to get a Riemannian manifold along with its associated geometric properties. We then use this Riemannian framework to apply object-oriented statistical analysis to define an atlas as the Fréchet mean of a population of Riemannian metrics. This formulation ties into the existing framework for diffeomorphic construction of image atlases, allowing us to construct a multimodal atlas by simultaneously integrating complementary white matter structure details from DWMRI and cortical details from T1-weighted MRI. We illustrate our framework with 2D data examples of connectome registration and atlas formation. Finally, we build an example 3D multimodal atlas using T1 images and connectomes derived from diffusion tensors estimated from a subset of subjects from the Human Connectome Project. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

GPU-Accelerated DNS of Compressible Turbulent Flows

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arXiv 2022年

作者： Kim, Youngdae Ghosh, Debojyoti Constantinescu, Emil M. Balakrishnan, Ramesh Computational Science Division Argonne National Laboratory 9700 S. Cass Ave. LemontIL60439 United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory 7000 East Ave. LivermoreCA94550 United States Mathematics and Computer Science Division Argonne National Laboratory 9700 S. Cass Ave. LemontIL60439 United States

This paper explores strategies to transform an existing CPU-based high-performance computational fluid dynamics solver, HyPar, for compressible flow simulations on emerging exascale heterogeneous (CPU+GPU) computing platforms. The scientific motivation for developing a GPU-enhanced version of HyPar is to simulate canonical turbulent flows at the highest res-MSC Codes 76F65, 65Y05, 76F05, 35Q30, 65M06 Copyright © 2022, The Authors. All rights reserved.

关键词： Turbulent flow

Kernel-based stochastic collocation for the random two-phase navier-stokes equations

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arXiv 2018年

作者： Griebel, M. Rieger, C. Zaspel, P. Institute for Numerical Simulation Bonn University Endenicher Allee 19b BonnD-53115 Fraunhofer Institute for Algorithms and Scientific Computing Scai Schloss Birlinghoven Sankt AugustinD-53754 Department of Mathematics Rwth Aachen University Schinkelstr. 2 AachenD-52062 Department of Mathematics and Computer Science University of Basel Spiegelgasse 1 Basel4051

In this work, we apply stochastic collocation methods with radial kernel basis functions for an uncertainty quantification of the random incompressible two-phase Navier-Stokes equations. Our approach is non-intrusive and we use the existing fluid dynamics solver NaSt3DGPF to solve the incompressible two-phase Navier-Stokes equation for each given realization. We are able to empirically show that the resulting kernel-based stochastic collocation is highly competitive in this setting and even outperforms some other standard methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Viscous flow

Mapping 3d Grain and Precipitate Structure During in Situ Mechanical Testing of Open-Cell Metal Foam Using Micro-Computed Tomography and High-Energy X-Ray Diffraction Microscopy

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SSRN

SSRN 2022年

作者： Johnson, Quinton C. Kenesei, Peter Petruzza, Steve Plumb, Jayden Sharma, Hemant Park, Jun-Sang Marsden, Elliott Matheson, Kristoffer Czabaj, Michael W. Spear, Ashley D. Department of Mechanical Engineering University of Utah Salt Lake CityUT84112 United States Scientific Computing and Imaging Institute University of Utah Salt Lake CityUT84112 United States Department of Computer Science Utah State University LoganUT84322 United States Advanced Photon Source Argonne National Laboratory LemontIL60439 United States

Open-cell metal foams are ultra-low-density cellular metals with complex hierarchical structures that span bulk, cell, ligament, and sub-ligament scales and give rise to desirable properties such as high strength-to-weight ratio and excellent energy absorption. Although literature suggests that intrinsic material structures at sub-ligament length scales (e.g., grains and precipitates) play an important role in mechanical behavior of open-cell metal foams, there are very few experimental measurements of such structures in three dimensions and for meaningful volumes of foam. This study seeks to map and track the three-dimensional (3D) grain and precipitate structures of an intact volume of open-cell aluminum foam by advancing microstructural characterization techniques that leverage X-ray micro-computed tomography ( µ CT) and far-field high-energy X-ray diffraction microscopy (FF-HEDM). A 6%-relative-density aluminum foam sample was mechanically tested in compression while µ CT and FF-HEDM measurements were collected at interrupted loading states at beamline 1-ID of the Advanced Photon Source. A new scanning strategy and reconstruction algorithm were established to enable characterization of a foam volume with diameter approximately four times wider than the nominal width of the X-ray beam. The result is a set of maps that detail both the 3D grain and precipitate structures throughout the foam volume at successive strain steps. A novel grain tracking procedure was developed to track individual grains within the foam volume by accounting for the large rigid-body motions that individual ligaments can undergo during mechanical loading. The ability to track grains and precipitate structures in three dimensions throughout large bulk deformation of ulta-low-density polycrystalline materials enables new possibilities for validating numerical models and investigating local failure mechanisms. Furthermore, the methods and procedures developed in this study could be applied to

关键词： Synchrotron radiation

CD2CR: Co-reference resolution across documents and domains

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arXiv 2021年

作者： Ravenscroft, James Cattan, Arie Clare, Amanda Dagan, Ido Liakata, Maria Centre for Scientific Computing University of Warwick CV4 7AL United Kingdom Department of Computer Science Aberystwyth University SY23 3DB United Kingdom Alan Turing Institute 96 Euston Rd LondonNW1 2DB United Kingdom Filament AI 1 King William St LondonEC4N 7BJ United Kingdom Computer Science Department Bar-Ilan University Ramat-Gan Israel

Cross-document co-reference resolution (CDCR) is the task of identifying and linking mentions to entities and concepts across many text documents. Current state-of-the-art models for this task assume that all documents are of the same type (e.g. news articles) or fall under the same theme. However, it is also desirable to perform CDCR across different domains (type or theme). A particular use case we focus on in this paper is the resolution of entities mentioned across scientific work and newspaper articles that discuss them. Identifying the same entities and corresponding concepts in both scientific articles and news can help scientists understand how their work is represented in mainstream media. We propose a new task and English language dataset for cross-document cross-domain co-reference resolution (CD2CR). The task aims to identify links between entities across heterogeneous document types. We show that in this cross-domain, cross-document setting, existing CDCR models do not perform well and we provide a baseline model that outperforms current state-of-the-art CDCR models on CD2CR. Our data set, annotation tool and guidelines as well as our model for cross-document cross-domain co-reference are all supplied as open access open source resources. © 2021, CC BY.

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