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arXiv 2018年

作者： Brennan, Meghan Puri, Sahil Ozrazgat-Baslanti, Tezcan Bhat, Rajendra Feng, Zheng Momcilovic, Petar Li, Xiaolin Wang, Daisy Zhe Bihorac, Azra Departments of Anesthesiology Medicine University of Florida College of Medicine GainesvilleFL United States Departments of Computer & Information Science & Engineering Electrical and Computer Engineering Industrial and Systems Engineering University of Florida Herbert Wertheim College of Engineering GainesvilleFL United States University of Florida GainesvilleFL United States

RO1GM110240 from the National Institute of General Medical Sciences. T.O.B. has received a grant (97071) from Clinical and Translational Science Institute, University of Florida. This work was supported in part by the NIH/NCATS Clinical and Translational Sciences Award to the University of Florida UL1 TR000064. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Society of Critical Care Medicine. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. A.B. and T.O.B. had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. The authors report no conflict of interest. The preliminary report from this research was presented orally at the 40th Annual Meeting of the Society of Critical Care Medicine. The authors declare no conflicts of interest. Copyright © 2018, The Authors. All rights reserved.

关键词： Risk assessment

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

arXiv

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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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Correction: Core–shell defective TiO 2 nanoparticles by femtosecond laser irradiation with enhanced photocatalytic performance

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Materials Advances 2023年第5期4卷 1403-1403页

作者： Bersu Bastug Azer Ahmet Gulsaran Joel R. Pennings Reza Karimi Aydin Ashrafi Belgabad Alexander H. Xu Liena Zaidan Samed Kocer Joseph Sanderson Michal Bajcsy Michael A. Pope Mustafa Yavuz Department of Mechanical and Mechatronics Engineering University of Waterloo 200 University Ave. West Waterloo ON N2L 3G1 Canada bbastuga@uwaterloo.ca myavuz@uwaterloo.ca Waterloo Institute for Nanotechnology (WIN) University of Waterloo 200 University Ave. West Waterloo ON N2L 3G1 Canada Department of Physics and Astronomy University of Waterloo 200 University Ave. West Waterloo ON N2L 3G1 Canada Department of Physics and Energy Engineering Amirkabir University of Technology (Tehran Polytechnic) P.O. Box 15875-4413 Tehran Iran Department of Nanotechnology Engineering University of Waterloo 200 University Ave. West Waterloo ON N2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo 200 University Ave. West Waterloo ON N2L 3G1 Canada Department of Systems Design Engineering University of Waterloo 200 University Ave. West Waterloo ON N2L 3G1 Canada Institute for Quantum Computing University of Waterloo 200 University Ave. West Waterloo ON N2L 3G1 Canada Department of Chemical Engineering University of Waterloo 200 University Ave. West Waterloo ON N2L 3G1 Canada

Correction for ‘Core–shell defective TiO 2 nanoparticles by femtosecond laser irradiation with enhanced photocatalytic performance’ by Bersu Bastug Azer et al. , Mater. Adv. , 2023, https://***/10.1039/d3ma00019b .

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Breast mass regions classification from mammograms using convolutional neural networks and transfer learning.

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Optica Acta: International Journal of Optics 2023年第10期70卷

作者： Yuliana Jiménez-Gaona María José Rodríguez-Álvarez Diana Carrión-Figueroa Darwin Castillo-Malla Vasudevan Lakshminarayanan a Departamento de Química y Ciencias Exactas Universidad Técnica Particular de Loja Loja Ecuadorb Instituto de Instrumentación para la Imagen Molecular i3M Universitat Politècnica de València (UPV)- Consejo Superior de Investigaciones Científicas (CSIC) Valencia Spaind Theoretical and Experimental Epistemology Lab School of Optometry and Vision Science University of Waterloo Waterloo Canada b Instituto de Instrumentación para la Imagen Molecular i3M Universitat Politècnica de València (UPV)- Consejo Superior de Investigaciones Científicas (CSIC) Valencia Spain c Hospital Carlos Andrade Marín IESS Quito Ecuador d Theoretical and Experimental Epistemology Lab School of Optometry and Vision Science University of Waterloo Waterloo Canadae Department of Systems Design Engineering Physics and Electrical and Computer Engineering University of Waterloo Waterloo Canada

This study introduces a novel approach aimed at enhancing the quality of digital mammography images through pre-processing techniques, to improve breast cancer detection accuracy. The primary objective is to enhance image resolution, thus leading to more precise breast tissue segmentation and subsequent classification utilizing convolutional neural networks (CNNs). Three recognized public mammography databases: CBIS-DDSM, Mini-MIAS, and Inbreast were used as pre-processing data. Our statistical findings revealed that the EDSR method (PSNR = 39.05 dB/ SSIM = 0.90) consistently outperformed the visual quality of images when compared to SR-RDN (PSNR = 32.68 dB/SSIM = 0.82). Similarly, UNet demonstrated superior performance over SegNet, boasting an average Intersection over Union (IoU) of 0.862, an average Dice coefficient of 0.991, and an accuracy rate of 0.947 in Region of Interest (RoI) segmentation results. In conclusion, the ResNet model contributed to enhanced accuracy compared to conventional machine learning algorithms. However, it did not surpass state-of-the-art deep CNN-based classifiers, achieving an accuracy rate of 75%.Abbreviations:AUC: Area under curve; CAD: computer aided system; CC: Cranio caudal; CNN: Convolutional neural network; DNN: Deep neural network; DDSM: Digital Database for Screening Mammography; DM: Digital mammography; DL: Deep learning; EDSR: Enhanced Deep Residual Network; E2E: End to End; ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks; ESPCN: Efficient sub-pixel convolutional neural network; GAN: Generative adversarial network; HR: High resolution; IoU: Intersection over Union; LR: Low resolution; MDSR: Multi-scale deep super-resolution; MLO: Mediolateral Oblique; PSNR: Peak signal to Noise Ratio; RoI: Region of interest; RDN: Residua Dense Network; RDB: Residual Dense Block; RNN: Recurrent Neural Network; ReLU: Rectified Linear Unit; SR-GAN: Super-Resolution Using a Generative Adversarial Network; SSIM: Structural Sim

关键词： Breast cancer classification convolutional neural network mammography images segmentation super resolution

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Toward the universal quantum cloner limit for designing compact photonic CNOT gate

arXiv

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arXiv 2019年

作者： Gueddana, Amor Lakshminarayanan, Vasudevan Green & Smart Communication Systems Lab Gres'Com Engineering School of Communication of Tunis Sup'Com University of Carthage Ghazela Technopark Ariana2083 Tunisia Theoretical & Experimental Epistemology Lab TEEL School of Optometry and Vision Science University of Waterloo 200 University Avenue West WaterlooONN2l 3G1 Canada Department of Physics Department of Electrical and Computer Engineering Department of Systems Design Engineering University of Waterloo 200 University Avenue West WaterlooONN2l 3G1 Canada

We suggest a near deterministic compact model of a photonic CNOT gate based on a quantum dot trapped in a double sided optical microcavity and a universal cloner. Our design surpasses the cloner optimal limit of 5/6 and we show that it provides fidelity around 91 % in the weak coupling regime. Copyright © 2019, The Authors. All rights reserved.

关键词： Semiconductor quantum dots

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A Modified Polynomial Chaos Modeling Approach for Uncertainty Quantification

A Modified Polynomial Chaos Modeling Approach for Uncertaint...

引用

International Applied Computational Electromagnetics Society Symposium

作者： Majid Ahadi Dolatsara Ambrish Varma Kumar Keshavan Madhavan Swaminathan School of Electrical and Computer Engineering Center for Co-Design of Chip Package System (C3PS) Georgia Institute of Technology Atlanta Ga Cadence Design Systems Inc. Chelmsford MA

ISBN: (纸本)9781728115184;9780996007887

Uncertainty quantification is a key element for modeling high speed circuits, which is often done with Monte Carlo analysis. However, because this method is computationally expensive, new approaches with higher efficiency have been developed. Many popular methods are based on the surrogate models developed with Polynomial Chaos theory. However, size of these models can be prohibitively large for realistic examples. Hence, this paper provides a novel methodology to use an ensemble of weaker models to improve efficiency. Finally, a numerical example with a DDR4 topology is provided.

关键词： DDR4 design space exploration Polynomial chaos theory Uncertainty quantification

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Optimal Power Allocation for Amplify and Forward Relaying with Finite Blocklength Codes and QoS Constraints

Optimal Power Allocation for Amplify and Forward Relaying wi...

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IEEE Conference on Vehicular Technology (VTC)

作者： Yulin Hu M. Cenk Gursoy Anke Schmeink Information Theory and Systematic Design of Communication Systems RWTH Aachen University Aachen Germany Department of Electrical Engineering and Computer Science Syracuse University Syracuse NY USA

In this work, motivated by emerging low-latency applications, we consider an amplify-and-forward relaying network operating with finite blocklength (FBL) codes subject to delay quality of service (QoS) constraints. Hence, we address both transmission delay (via FBL codes) and queueing delay (via delay QoS requirements). We first derive the QoS-constrained throughput of the network. Subsequently, we state a resource allocation problem aiming at allocating the power between the source and the relay to maximize the throughput. The convexity of the problem is proved and the optimal power allocation policy is provided. Via simulations, we confirm the accurateness of our analytical model. In addition, we provide interesting insights on the system behavior by characterizing the impact of the error probability, the QoS-exponent and coding blocklength on the throughput performance.

关键词： Quality of service Throughput Relays Resource management Error probability Encoding Delays

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The application of an extracellular vesicle-based biosensor in early diagnosis and prediction of chemoresponsiveness in ovarian cancer

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Cancer Letters 2024年 581卷 216533-216533页

作者： Tsang, Benjamin K. Asare-Werehene, Meshach Hunter, Rob Gerber, Emma Reunov, Arkadiy Brine, Isaiah Chang, Chia-Yu Chang, Chia-Ching Shieh, Dar-Bin Burger, Dylan Anis, Hanan Departments of Obstetrics & Gynecology and Cellular & Molecular Medicine Centre for Infection Immunity and Inflammation Interdisciplinary School of Health Sciences University of Ottawa Ottawa ON K1N 6N5 Canada Chronic Disease Program Ottawa Hospital Research Institute The Ottawa Hospital Ottawa ON K1Y 4E9 Canada Department of Biology St. Francis Xavier University 2320 Notre Dame Avenue Antigonish NS B2G 2W5 Canada School of Electrical Engineering and Computer Science Ottawa-Carleton Institute for Biomedical Engineering University of Ottawa Ottawa ON K1N 6N5 Canada Department of Biological Science and Department of Electrophysics Technology Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) National Yang Ming Chiao Tung University Hsinchu 30068 Taiwan Institute of Basic Medical Science Institute of Oral Medicine and Department of Stomatology Advanced Optoelectronic Technology Center and Center for Micro/Nano Science and Technology National Cheng Kung University Hospital National Cheng Kung University Tainan 704 Taiwan Institute of Physics Academia Sinica Taipei 10529 Taiwan

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Inverse design Methods: Inverse design Strategies for 3D Surfaces Formed by Mechanically Guided Assembly (Adv. Mater. 14/2020)

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Advanced Materials 2020年第14期32卷

作者： Zhichao Fan Yiyuan Yang Fan Zhang Zheng Xu Hangbo Zhao Taoyi Wang Honglie Song Yonggang Huang John A. Rogers Yihui Zhang Applied Mechanics Laboratory Department of Engineering Mechanics Center for Flexible Electronics Technology Tsinghua University Beijing 100084 P. R. China Departments of Mechanical Engineering Northwestern University Evanston IL 60208 USA The State Key Laboratory for Manufacturing and Systems Engineering School of Mechanical Engineering Xi'an Jiaotong University Xi'an 710049 P. R. China Center for Bio‐Integrated Electronics Northwestern University Evanston IL 60208 USA Department of Physics Tsinghua University Beijing 100084 P. R. China Departments of Civil and Environmental Engineering Mechanical Engineering and Materials Science and Engineering Center for Bio‐Integrated Electronics Northwestern University Evanston IL 60208 USA Department of Materials Science and Engineering Biomedical Engineering Neurological Surgery Chemistry Mechanical Engineering Electrical Engineering and Computer Science Simpson Querrey Institute and Feinberg Medical School Center for Bio‐Integrated Electronics Northwestern University Evanston IL 60208 USA

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Eye tracking: empirical foundations for a minimal reporting guideline

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Behavior research methods 2023年第1期55卷 364-416页

作者： Kenneth Holmqvist Saga Lee Örbom Ignace T C Hooge Diederick C Niehorster Robert G Alexander Richard Andersson Jeroen S Benjamins Pieter Blignaut Anne-Marie Brouwer Lewis L Chuang Kirsten A Dalrymple Denis Drieghe Matt J Dunn Ulrich Ettinger Susann Fiedler Tom Foulsham Jos N van der Geest Dan Witzner Hansen Samuel B Hutton Enkelejda Kasneci Alan Kingstone Paul C Knox Ellen M Kok Helena Lee Joy Yeonjoo Lee Jukka M Leppänen Stephen Macknik Päivi Majaranta Susana Martinez-Conde Antje Nuthmann Marcus Nyström Jacob L Orquin Jorge Otero-Millan Soon Young Park Stanislav Popelka Frank Proudlock Frank Renkewitz Austin Roorda Michael Schulte-Mecklenbeck Bonita Sharif Frederick Shic Mark Shovman Mervyn G Thomas Ward Venrooij Raimondas Zemblys Roy S Hessels Department of Psychology Nicolaus Copernicus University Torun Poland. kenneth.holmqvist@ur.de. Department of Computer Science and Informatics University of the Free State Bloemfontein South Africa. kenneth.holmqvist@ur.de. Department of Psychology Regensburg University Regensburg Germany. kenneth.holmqvist@ur.de. Department of Psychology Regensburg University Regensburg Germany. Experimental Psychology Helmholtz Institute Utrecht University Utrecht The Netherlands. Lund University Humanities Lab and Department of Psychology Lund University Lund Sweden. Department of Ophthalmology SUNY Downstate Health Sciences University Brooklyn NY USA. Tobii Pro AB Danderyd Sweden. Social Health and Organizational Psychology Utrecht University Utrecht The Netherlands. Department of Computer Science and Informatics University of the Free State Bloemfontein South Africa. TNO Soesterberg The Netherlands. Department of Ergonomics Leibniz Institute for Working Environments and Human Factors Dortmund Germany. Institute of Informatics LMU Munich Munich Germany. Institute of Child Development University of Minnesota Minneapolis USA. School of Psychology University of Southampton Southampton UK. School of Optometry and Vision Sciences Cardiff University Cardiff UK. Department of Psychology University of Bonn Bonn Germany. Vienna University of Economics and Business Vienna Austria. Department of Psychology University of Essex Essex UK. Department of Neuroscience Erasmus MC Rotterdam The Netherlands. Machine Learning Group Department of Computer Science IT University of Copenhagen Copenhagen Denmark. SR Research Ltd Ottawa Canada. Human-Computer Interaction University of Tübingen Tübingen Germany. University of British Columbia Columbia Canada. Department of Eye and Vision Science Institute of Life Course and Medical Sciences University of Liverpool Liverpool UK. Department of Education and Pedagogy Division Education Faculty of Social and Behavioral Sciences Ut

In this paper, we present a review of how the various aspects of any study using an eye tracker (such as the instrument, methodology, environment, participant, etc.) affect the quality of the recorded eye-tracking data and the obtained eye-movement and gaze measures. We take this review to represent the empirical foundation for reporting guidelines of any study involving an eye tracker. We compare this empirical foundation to five existing reporting guidelines and to a database of 207 published eye-tracking studies. We find that reporting guidelines vary substantially and do not match with actual reporting practices. We end by deriving a minimal, flexible reporting guideline based on empirical research (Section "An empirically based minimal reporting guideline").

关键词： Data quality Eye movements Eye tracking Replicability Reporting guidelines Reporting practices Reporting standards Reproducibility

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