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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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MacWilliams identities of linear codes with respect to RT metric over Mn×s (F2[u, v]{uk, v2, uv-Vu})

MacWilliams identities of linear codes with respect to RT me...

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作者： Shi, Minjia Tang, Jie Ge, Maorong Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University No. 3 Feixi Road Hefei Anhui Province230039 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China School of Mathematical Sciences Anhui University Hefei230601 China

The definitions of the Lee complete ρ weight enumerator and the exact complete ρ weight enumerator over Mn×s (F2[u, v]{uk, v2, uv-vu}) are introduced, and the MacWilliams identities with respect to the RT metric for these two weight enumerators of linear codes over Mn×s (F2[u, v]{uk, v2, uv-vu}) are obtained. Finally, we give two examples to illustrate the results we obtained. Copyright © 2017 The Institute of Electronics, Information and Communication Engineers.

关键词： Codes (symbols)

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On self-dual and LCD double circulant and double negacirculant codes over Fq+ uFq

arXiv

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arXiv 2018年

作者： Shi, Minjia Zhu, Hongwei Qian, Liqin Sok, Lin Sol, Patrick Key Laboratory of Intelligent Computing Signal Processing Ministry of Education Anhui University No.3 Feixi Road Hefei Anhui230039 China School of Mathematical Sciences Anhui University Hefei Anhui230601 China National Mobile Communications Research Laboratory Southeast University China Department of Mathematics Royal University of Phnom Penh Phnom Penh12156 Cambodia CNRS LAGA University of Paris 8 Saint-Denis93526 France

Double circulant codes of length 2n over the semilocal ring R = Fq + uFq, u2 = u, are studied when q is an odd prime power, and-1 is a square in Fq. Double negacirculant codes of length 2n are studied over R when n is even and q is an odd prime power. Exact enumeration of self-dual and LCD such codes for given length 2n is given. Employing a duality-preserving Gray map, self-dual and LCD codes of length 4n over Fq are constructed. Using random coding and the Artin conjecture, the relative distance of these codes is bounded below. The parameters of examples of the modest length are computed. Several such codes are optimal. Copyright © 2018, The Authors. All rights reserved.

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Double circulant self-dual and LCD codes over Galois rings

arXiv

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arXiv 2018年

作者： Shi, Minjia Huang, Daitao Sok, Lin Solé, Patrick School of Mathematical Sciences Anhui University Hefei230601 China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University No. 3 Feixi Road Hefei Anhui Province230039 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Anhui University Hefei230601 China CNRS/LAGA University of Paris 8 2 rue de la Liberté Saint-Denis93 526 France

This paper investigates the existence, enumeration and asymptotic performance of self-dual and LCD double circulant codes over Galois rings of characteristic p2 and order p4 with p and odd prime. When p ≡ 3 (mod 4), we give an algorithm to construct a duality preserving bijective Gray map from such a Galois ring to Z2p2. Using random coding, we obtain families of asymptotically good self-dual and LCD codes over Zp2, for the metric induced by the standard Fp-valued Gray maps. Copyright © 2018, The Authors. All rights reserved.

关键词： Codes (symbols)

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Common Limitations of Image processing Metrics: A Picture Story

arXiv

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arXiv 2021年

作者： Reinke, Annika Tizabi, Minu D. Sudre, Carole H. Eisenmann, Matthias Rädsch, Tim Baumgartner, Michael Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Bankhead, Peter Benis, Arriel Blaschko, Matthew Buettner, Florian Cardoso, M. Jorge Chen, Jianxu Cheplygina, Veronika Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Engelhardt, Sandy Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hamprecht, Fred Hashimoto, Daniel A. Heckmann-Nötzel, Doreen Hirsch, Peter Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kavur, A. Emre Kenngott, Hannes Kleesiek, Jens Kleppe, Andreas Koehler, Sven Kofler, Florian Kopp-Schneider, Annette Kooi, Thijs Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moher, David Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Noyan, M. Alican Petersen, Jens Polat, Gorkem Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Rieke, Nicola Riegler, Michael A. Rivaz, Hassan Saez-Rodriguez, Julio Sánchez, Clara I. Schroeter, Julien Saha, Anindo Selver, M. Alper Sharan, Lalith Shetty, Shravya Smeden, Maarten V.A.N. Stieltjes, Bram Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben V.A.N. Varoquaux, Gaël Wiesenfarth, Manuel Yaniv, Ziv R. Jäger, Paul Maier-Hein, Lena Division of Intelligent Medical Systems and HI Helmholtz Imaging Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Intelligent Medical Systems Heidelberg Germany NCT Heidelberg DKFZ University Medical Center Heidelberg Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Division of Medical Image Computing Heidelberg Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Institute of Genetics and Cancer University of Edinburgh Edinburgh United Kingdom Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 - box 2441 Leuven3001 Belgium Frankfurt/Mainz DKFZ UCT Frankfurt-Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany Frankfurt Cancer Insititute Germany Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for St

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide. © 2021, CC BY.

关键词： Medical imaging

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High dynamic range imaging technology

arXiv

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arXiv 2017年

作者： Artusi, Alessandro Richter, Thomas Ebrahimi, Touradj Mantiuk, Rafal K. KIOS Research and Innovation Center University of Cyprus TIK Computing Center University of Stuttgart Ecole Poly-technique Fédérale de Lausanne heading its Multimedia Signal Processing group Computer Laboratory University of Cambridge United Kingdom

IN this lecture note, we describe high dynamic range (HDR) imaging systems;such systems are able to represent luminances of much larger brightness and, typically, also a larger range of colors than conventional standard dynamic range (SDR) imaging systems. The larger luminance range greatly improve the overall quality of visual content, making it appears much more realistic and appealing to observers. HDR is one of the key technologies of the future imaging pipeline, which will change the way the digital visual content is represented and manipulated today. Copyright © 2017, The Authors. All rights reserved.

关键词： Luminance

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A New 4-D Hyperchaotic System with Four-Scroll Hidden Attractor, Its Properties and Bifurcation Analysis

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IOP Conference Series: Materials Science and Engineering 2019年第1期621卷

作者： L G Dolvis S Vaidyanathan K Jacques A Sambas Sukono M Mamat Laboratory of Condensed Matter Electronics and Signal Processing Faculty of Science Department of Physics University of Dschang Dschang CAMEROON Research and Development Centre Vel Tech University Chennai Tamil Nadu INDIA Department of Electrical Engineering IUT FV Bandjoun University of Dschang Dschang CAMEROON Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya INDONESIA Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Padjadjaran INDONESIA Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Kuala Terengganu MALAYSIA

This paper announces a new four-dimensional hyperchaotic system with a four-scroll attractor and discusses its dynamic properties such as Lyapunov exponents, phase portraits, Kaplan-Yorke dimension and equilibrium points. Our calculations show that the new hyperchaotic system has no equilibrium point and hence it exhibits hidden attractor. Our new hyperchaotic system has three nonlinearities in total. A detailed bifurcation analysis has been presented for the new hyperchaotic system with four-scroll hidden attractor. Specifically, we discussed bifurcation analysis such as route to four-scroll hyperchaos, coexisting bifurcation, multistability, two parameter Lyapunov exponents and antimonotonicity.

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Reconfigurable accelerator design platform for ultrasonic signal processing and imaging applications

Reconfigurable accelerator design platform for ultrasonic si...

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IEEE International Ultrasonics Symposium

作者： Paul Gal Jafar Saniie Embedded Computing and Signal Processing Research Laboratory Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago U.S.A.

ISBN: (纸本)9781467398985

Ultrasonic testing and imaging systems have evolved from a basic single transducer system to arrays capable of 3-dimensional scans. These systems need to be flexible and also reconfigurable in order to acquire and process a vast amount of data. The system must also allow for a variety of parameters like the data acquisition rate, signal lengths and processing characteristics which need to be reconfigurable for adaptability to different applications. This study presents a reconfigurable hardware/software co-design system including specific hardware accelerators to acquire and perform ultrasonic signal processing efficiently. For this study, the architecture of the developed ultrasonic system is comprised of two different functional components. One component is specially designed to process the data while the other component gathers, compresses and transfers data which can later be restored and analyzed. For data transfer a simple and consequently efficient real-time operating system and Ethernet connective is developed. This is all implemented on the ZEDBoard Zynq SoC for maximum flexibility and performance. The data handling component is built with support for gathering, compressing, reconstructing, and transferring data to other platforms via Ethernet communication. The different variations of the designs allow the system designers the choice of applying the best solution for their specific applications.

关键词： Acoustics Discrete wavelet transforms Adders signal processing Field programmable gate arrays Real-time systems Image reconstruction Field programmable gate arrays discrete wavelet transform adders signal processing configurability real-time systems image reconstruction Acoustics ultrasonic signal ethernet communication hardware accelerator

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Construction of One-Gray Weight Codes and Two-Gray Weight Codes over Z4+uZ4

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Journal of Systems Science & Complexity 2016年第5期29卷 1472-1484页

作者： SHI Minjia WANG Dandan GAO Jian WU Bo Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei 230039 China National Mobile Communications Research Laboratory Southeast University Nanjing 210096China School of Mathematical Sciences Anhui University Hefei 230601 China. School of Science Shandong University of Technology Zibo 255091 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Bcijing 100093 China

This paper firstly gives some necessary conditions on one-Gray weight linear codes. And then we use these results to construct several classes of one-Gray weight linear codes over Z_4 +uZ_4(u^2=u) with type 16^(k_1)8^(k_2)8^(k_3)4^(k_4)4^(k_5)4^(k_6)2^(k_7)2^(k_8) based on a distance-preserving Gray map from(Z4 + u Z4)n to Z2n4. Secondly, the authors use the similar approach to do works on two-Gray(projective) weight linear codes. Finally, some examples are given to illustrate the construction methods.

关键词： Generator matrix Gray map linear code one-Gray weight code two-Gray weight code

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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