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Pre-screening for non-diagnostic coronary computed tomography angiography

European heart journal. Imaging methods and practice 2023年第2期1卷 qyad026页

作者： Ramtin Hakimjavadi Juan Lu Yeung Yam Girish Dwivedi Gary R Small Benjamin J W Chow Department of Medicine Division of Cardiology University of Ottawa Heart Institute 40 Ruskin Street Ottawa ON K1Y 4W7 Canada. Department of Medicine The University of Western Australia 35 Stirling Highway CRAWLEY Western Australia 6009 Australia. Department of Computer Science and Software Engineering The University of Western Australia 35 Stirling Highway CRAWLEY Western Australia 6009 Australia. Advanced Clinical and Translational Cardiovascular Imaging Harry Perkins Institute of Medical Research 6 Verdun Street Nedlands Western Australia 6009 Australia. Department of Medicine Fiona Stanley Hospital 11 Robin Warren Drive Murdoch Western Australia 6150 Australia. Department of Radiology University of Ottawa 451 Smyth Rd Ottawa ON K1H 8M5 Canada.

Aims:Indiscriminate coronary computed tomography angiography (CCTA) referrals for suspected coronary artery disease could result in a higher rate of equivocal and non-diagnostic studies, leading to inappropriate downstream resource utilization or delayed time to diagnosis. We sought to develop a simple clinical tool for predicting the likelihood of a non-diagnostic CCTA to help identify patients who might be better served with a different test. Methods and results:We developed a clinical scoring system from a cohort of 21 492 consecutive patients who underwent CCTA between February 2006 and May 2021. Coronary computed tomography angiography study results were categorized as normal, abnormal, or non-diagnostic. Multivariable logistic regression analysis was conducted to produce a model that predicted the likelihood of a non-diagnostic test. Machine learning (ML) models were utilized to validate the predictor selection and prediction performance. Both logistic regression and ML models achieved fair discriminate ability with an area under the curve of 0.630 [95% confidence interval (CI) 0.618-0.641] and 0.634 (95% CI 0.612-0.656), respectively. The presence of a cardiac implant and weight >100 kg were among the most influential predictors of a non-diagnostic study. Conclusion:We developed a model that could be implemented at the 'point-of-scheduling' to identify patients who would be best served by another non-invasive diagnostic test.

关键词： cardiac imaging techniques coronary computed tomographic angiography image interpretation non-diagnostic tests prediction model

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Multi-scale neural networks for retinal blood vessels segmentation

arXiv

引用

arXiv 2018年

作者： Zhang, Boheng Huang, Shenglei Hu, Shaohan MOE Key Laboratory of Bioinformatics and Bioinformatics Division TNLIST Department of Automation Tsinghua University Department of Computer Science and Engineering Shanghai Jiao Tong University School of Software Tsinghua University

Existing supervised approaches didn't make use of the low-level features which are actually effective to this task. And another deficiency is that they didn't consider the relation between pixels, which means effective features are not extracted. In this paper, we proposed a novel convolutional neural network which make sufficient use of low-level features together with high-level features and involves atrous convolution to get multi-scale features which should be considered as effective features. Our model is tested on three standard benchmarks - DRIVE, STARE, and CHASE databases. The results presents that our model significantly outperforms existing approaches in terms of accuracy, sensitivity, specificity, the area under the ROC curve and the highest prediction speed. Our work provides evidence of the power of wide and deep neural networks in retinal blood vessels segmentation task which could be applied on other medical images tasks. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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Possibilities about the design Lean canvas model and its adaptation in the agile testing

Possibilities about the design Lean canvas model and its ada...

引用

2017 Symposium for Young Scientists in Technology, engineering and Mathematics, SYSTEM 2017

作者： Nidagundi, Padmaraj Novickis, Leonids Faculty of Computer Science and Information Technology Institute of Applied Computer Systems Riga Technical University Riga Latvia Head of the Division of Software Engineering Faculty of Computer Science and Information Technology Riga Technical University Riga Latvia

software development industry is inventing new development methodologies in last 10 years for improving the existing developing tools, process and delivering a high-quality software to end customer in short time. In recent year's waterfall development becomes less attractive in the software industry and shifted its attention towards the new software development methodologies such as agile. Agile brought the new dimension to software development and improved the development, testing process and added new challenges. There are different agile development methodologies in practice such as Scrum, FDD, Kanban, DSDM, XP, ASD, Lean, and Crystal, all these have different approaches in software development but share the same principle of the agile. Nowadays software development process adopted a new number of software tools in the overall process of development and software testing also moved towards the more automation to deploy, validate and verify the software code as early possible. In recent years, lean canvas is used for the planning and validating the business model and this paper will bring more focus on the lean canvas model design adaptation in agile software test strategy building, test planning and guiding the test team to work fast. Copyright © 2017 held by the authors.

关键词： software testing

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Effective capacity for renewal service processes with applications to HARQ systems

arXiv

引用

arXiv 2018年

作者： Shi, Zheng Tsiftsis, Theodoros Tan, Weiqiang Yang, Guanghua Ma, Shaodan Alouini, Mohamed-Slim School of Electrical and Information Engineering Jinan University Zhuhai519070 China School of Computer Science and Educational Software Guangzhou University Guangzhou510006 China Department of Electrical and Computer Engineering University of Macau Macao China CEMSE Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

Considering the widespread use of effective capacity in cross-layer design and the extensive existence of renewal service processes in communication networks, this paper thoroughly investigates the effective capacity for renewal processes. Based on Z-transform, we derive exact analytical expressions for the effective capacity at a given quality of service (QoS) exponent for both the renewal processes with constant reward and with variable rewards. Unlike prior literature that the effective capacity is approximated with no many insightful discussions, our expression is simple and reveals further meaningful results, such as the monotonicity and bounds of effective capacity. The analytical results are then applied to evaluate the cross-layer throughput for diverse hybrid automatic repeat request (HARQ) systems, including fixed-rate HARQ (FR-HARQ, e.g., Type I HARQ, HARQ with chase combining (HARQ-CC) and HARQ with incremental redundancy (HARQ-IR)), variable-rate HARQ (VR-HARQ) and cross-packet HARQ (XP-HARQ). Numerical results corroborate the analytical ones and prove the superiority of our proposed approach. Furthermore, targeting at maximizing the effective capacity via the optimal rate selection, it is revealed that VR-HARQ and XP-HARQ attain almost the same performance, and both of them perform better than FR-HARQ. Copyright © 2018, The Authors. All rights reserved.

关键词： Quality of service

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Spectroscopy-Based Food Internal Quality Evaluation with XGBoost Algorithm

引用

Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data, APWeb-WAIM 2018

作者： Li, Lingling Li, Caihong Wu, Yuan Yang, Yi Zhang, Yixuan Zhang, Hairui Wu, Bin Liu, Li Xie, Rong School of Information Science and Engineering Lanzhou University Lanzhou730000 China Silk Road Economic Belt Research Center Lanzhou University Lanzhou730000 China Division of Biological Science University of California at San Diego La Jolla United States Communication and Network Center Lanzhou University Lanzhou730000 China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing400044 China School of Software Engineering Chongqing University Chongqing400044 China School of Computer Science Wuhan University Wuhan430072 China

ISBN: (纸本)9783030012977

In this paper, the combination of Near-Infrared (NIR) spectroscopy and a novel forecasting algorithm called XGBoost was proposed for food internal quality evaluation. First, the original NIR spectral data was preprocessed by Savitzky-Golay smoothing method to reduce the influence of noises. Secondly, the preprocessed spectra was submitted to PCA to extract essential information. Finally, the model was established by using the XGBoost algorithm. The performance of the proposed model was examined by comparing with different models including back propagation neural network (BPNN) and support vector regression (SVR). The results showed that the new proposed model outperformed other two models and this XGBoost-based tool was suitable for food internal quality control. © 2018, Springer Nature Switzerland AG.

关键词： Near infrared spectroscopy

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MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for computer Vision

arXiv

引用

arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

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Involving external stakeholders in project courses

arXiv

引用

arXiv 2018年

作者： Steghöfer, Jan-Philipp Burden, Hakan Hebig, Regina Calikli, Gul Feldt, Robert Hammouda, Imed Horkoff, Jennifer Knauss, Eric Liebel, Grischa University of Gothenburg Sweden RISE Viktoria Sweden University of Gothenburg Sweden University of Gothenburg Sweden Mediterranean Institute of Technology South Mediterranean University Tunisia Software Engineering Division Department of Computer Science and Engineering Chalmers | University of Gothenburg Sweden

Problem: The involvement of external stakeholders in capstone projects and project courses is desirable due to its potential positive effects on the students. Capstone projects particularly profit from the inclusion of an industrial partner to make the project relevant and help students acquire professional skills. In addition, an increasing push towards education that is aligned with industry and incorporates industrial partners can be observed. However, the involvement of external stakeholders in teaching moments can create friction and could, in the worst case, lead to frustration of all involved parties. Contribution: We developed a model that allows analysing the involvement of external stakeholders in university courses both in a retrospective fashion, to gain insights from past course instances, and in a constructive fashion, to plan the involvement of external stakeholders. Key Concepts: The conceptual model and the accompanying guideline guide the teachers in their analysis of stakeholder involvement. The model is comprised of several activities (define, execute, and evaluate the collaboration). The guideline provides questions that the teachers should answer for each of these activities. In the constructive use, the model allows teachers to define an action plan based on an analysis of potential stakeholders and the pedagogical objectives. In the retrospective use, the model allows teachers to identify issues that appeared during the project and their underlying causes. Drawing from ideas of the reflective practitioner, the model contains an emphasis on reflection and interpretation of the observations made by the teacher and other groups involved in the courses. Key Lessons: Applying the model retrospectively to a total of eight courses shows that it is possible to reveal hitherto implicit risks and assumptions and to gain a better insight into the interaction between external stakeholders and students. Our empirical data reveals seven recurring risk them

关键词： Students

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New extension constructions of optimal frequency hopping sequence sets

arXiv

引用

arXiv 2018年

作者： Niu, Xianhua Xing, Chaoping National Key Laboratory of Science and Technology on Communications School of Computer and Software Engineering Xihua University University of Electronic Science and Technology of China Chengdu China Division of Mathematical Sciences School of Physical and Mathematical Sciences Nanyang Technological University Singapore. Singapore

In this paper, a general framework of constructing optimal frequency hopping sequence (FHS) sets is presented based on the designated direct product. Under the framework, we obtain infinitely many new optimal FHS sets by combining a family of sequences that are newly constructed in this paper with some known optimal FHS sets. Our constructions of optimal FHS sets are also based on extension method. However, our constructions remove the constraint requiring that the extension factor is co-prime with the length of original FHSs and get new parameters. In literature, most of the extension constructions suffer from this constraint. As a result, our constructions allow a great flexibility of choosing parameters of FHS sets for a given frequency-hopping spread spectrum system. Copyright © 2018, The Authors. All rights reserved.

关键词： Frequency hopping

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HCVMultiscaleFit: A Simulator For Parameter Estimation in Multiscale Models Of Hepatitis C Virus Dynamics

引用

AIP conference proceedings 2020年第1期2293卷 420028页

作者： Alexander Churkin Vladimir Reinharz Stephanie Lewkiewicz Harel Dahari Danny Barash Department of Software Engineering Sami Shamoon College of Engineering Beer-Sheva Israel. Center for Soft and Living Matter Institute for Basic Science Ulsan South Korea. Department of Mathematics University of California at Los Angeles Los Angeles California USA. The Program for Experimental and Theoretical Modeling Division of Hepatology Department of Medicine Stritch School of Medicine Loyola University Chicago Maywood Illinois USA. Department of Computer Science Ben-Gurion Universty Beer-Sheva Israel.

Callibration in mathematical models that are based on differential equations is known to be of fundamental importance. For sophisticated models such as age-structured models that simulate biological agents, parameter estimation or fitting (callibration) that solves all cases of data points available presents a formidable challenge, as efficiency considerations need to be employed in order for the method to become practical. In the case of multiscale models of hepatitis C virus dynamics that deal with partial differential equations (PDEs), a fully numerical parameter estimation method was developed that does not require an analytical approximation of the solution to the multiscale model equations, avoiding the necessity to derive the long-term approximation for each model. However, the method is considerably slow because of precision problems in estimating derivatives with respect to the parameters near their boundary values, making it almost impractical for general use. In order to overcome this limitation, two steps have been taken that significantly reduce the running time by orders of magnitude and thereby lead to a practical method. First, constrained optimization is used, letting the user add constraints relating to the boundary values of each parameter before the method is executed. Second, optimization is performed by derivative-free methods, eliminating the need to evaluate expensive numerical derviative approximations. These steps that were successful in significantly speeding up a highly non-efficient approach, rendering it practical, can also be adapted to multiscale models of other viruses and other sophisticated differential equation models. The newly efficient methods that were developed as a result of the above approach are described. Illustrations are provided using a user-friendly simulator that incorporates the efficient methods for multiscale models. We provide a simulator called HCVMultiscaleFit with a Graphical User Interface that applies these

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Retraction notice to “Improvement Mechanism of resistance random access memory with supercritical CO 2 fluid treatment” [J. Supercrit. Fluids, Volume 85, January 2014, Pages 183–189]

引用

The Journal of Supercritical Fluids 2021年 178卷

作者： Kuan-Chang Chang Jung-Hui Chen Tsung-Ming Tsai Ting-Chang Chang Syuan-Yong Huang Rui Zhang Kai-Huang Chen Yong-En Syu Geng-Wei Chang Tian-Jian Chu Guan-Ru Liu Yu-Ting Su Min-Chen Chen Jhih-Hong Pan Kuo-Hsiao Liao Ya-Hsiang Tai Tai-Fa Young Simon M. Sze Jen-Wei Huang Department of Materials and Optoelectronic Science National Sun Yat-Sen University 70 Lien-hai Road Kaohsiung 804 Taiwan Department of Chemistry National Kaohsiung Normal University Kaohsiung Taiwan Department of Physics National Sun Yat-Sen University 70 Lien-hai Road Kaohsiung 804 Taiwan School of Software and Microelectronics Peking University Beijing 100871 People's Republic of China Department of Electronics Engineering and Computer Science Tung-Fang Design Institute Kaohsiung Taiwan Department of Photonics and the Institute of Electro-Optical Engineering National Chiao Tung University Hsinchu 300 Taiwan Department of Mechanical and Electro-Mechanical Engineering Kaohsiung 804 Taiwan Department of Electronics Engineering and Institute of Electronics National Chiao Tung University Hsinchu 300 Taiwan Advanced Optoelectronics Technology Center National Cheng Kung University Taiwan Physics Division Institute of Nuclear Energy Research Taoyuan Taiwan Department of Physics ROC Military Academy Kaohsiung 83055 Taiwan

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