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QoS-aware interference management for vehic-ular D2D relay networks

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Journal of Communications and information Networks 2017年第2期2卷 75-90页

作者： Tinghan Yang Xiang Cheng Xia Shen Shanzhi Chen Liuqing Yang State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China State Key Laboratory of Integrated Services Networks Xidian UniversityXi'an 710126China China Academy of Information and Communication Technology Beijing 100191China State Key Laboratory of Wireless Mobile Communications China Academy of Telecommunications Technology(CATT)Beijing 100094China Department of Electrical and Computer Engineering Colorado State UniversityFort Collins CO 80523USA

Vehicular network communication technology is currently attracting a considerable amount of *** consider a scenario in which vehicular communication nodes share the same spectrum resources and generate interference with other *** with traditional interference-avoiding vehicular communications,this paper aims to increase the number of accessed communication links under the premise of satisfying the required *** our research,communication nodes have opportunities to select relay nodes to both help improve their data transmissions and reduce their transmit power in order to decrease interference with other links while still satisfying their QoS *** on these objectives,we propose an innovative interference management method that considers link selection,power adaption,and communication mode selection simultaneously to maximize the number of communication links with the lowest power *** with traditional link-selection and power-adaption interference management schemes,the proposed scheme improves QoS satisfaction with high energy *** results demonstrate both the effciency and the e ectiveness of the proposed scheme.

关键词： vehicular networks interference management link selection power adaption energy eciency

Event-triggered active-passive dynamic consensus filters

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Event-triggered active-passive dynamic consensus filters

American Control Conference

作者： J. Daniel Peterson Tansel Yucelen S. Jagannathan Eduardo Pasiliao the Mechanical and Aerospace Engineering Department and a member of the Advanced Systems Research Laboratory at the Missouri University of Science and Technology Rolla MO 65409 USA the Mechanical Engineering Department and the Director of the Laboratory for Autonomy Control Information and Systems at the University of South Florida Tampa FL 33620 USA the William A. Rutledge Distinguished Professor of the Electrical and Computer Engineering Department and Director of the Embedded Control Systems and Networking Laboratory at the Missouri University of Science and Technology Rolla MO 65409 USA the Air Force Research Laboratory Munitions Directorate Eglin Air Force Base FL 32542 USA

ISBN: (纸本)9781509045839

Multiagent systems consist of groups of agents that exchange local information to achieve given system-level goals. Traditionally, these systems assumed that all agents have some knowledge (e.g., observations of a process of interest) to contribute in order to achieve a common goal of interest, which may not be the case in many scenarios. Motivated from this standpoint, a new class of multiagent systems, active-passive multiagent systems, were recently proposed, which consists of agents subject to observations of a process of interest (i.e., active agents) and agents without any observations (i.e., passive agents). However, in this class of multiagent systems, agents need to continuously exchange information among themselves, incurring a high cost of interagent information exchange, and requiring that agents synchronize their update times. To address these shortcomings, this paper presents a new class of event-triggered active-passive dynamic consensus filters, in which agents schedule information exchange dependent on errors exceeding user-defined thresholds, significantly reducing the overall cost of interagent information exchange, and allowing agents to determine when to broadcast their information to their neighbors thus eliminating the need to synchronize their states. An illustrative numerical example is presented to demonstrate the efficacy of our approach.

关键词： consist groups agents

Comment on "Inherent security of phase coding quantum key distribution systems against detector blinding attacks" [Laser Phys. Lett. 15, 095203 (2018)]

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arXiv 2018年

作者： Fedorov, Aleksey Gerhardt, Ilja Huang, Anqi Jogenfors, Jonathan Kurochkin, Yury Lamas-Linares, Antía Larsson, Jan-Åke Leuchs, Gerd Lydersen, Lars Makarov, Vadim Skaar, Johannes Russian Quantum Center Skolkovo Moscow143025 Russia QRate Skolkovo Moscow143025 Russia QApp Skolkovo Moscow143025 Russia Institute of Physics University of Stuttgart Institute for Quantum Science and Technology Pfaffenwaldring 57 StuttgartD-70569 Germany Max Planck Institute for Solid State Research Heisenbergstraße 1 StuttgartD-70569 Germany Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Department of Electrical Engineering Linköping University LinköpingSE-58183 Sweden Texas Advanced Computing Center University of Texas at Austin AustinTX United States Max Planck Institute for the Science of Light University of Erlangen-Nürnberg ErlangenD-91058 Germany Kringsjåvegen 3E TrondheimNO-7032 Norway National University of Science and Technology MISIS Moscow119049 Russia Department of Technology Systems University of Oslo Box 70 KjellerNO-2027 Norway

Concerted alterations of functional connectivity and WM integrity in relationship to early amyloid deposition

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Alzheimer's & Dementia 2022年第S1期18卷

作者： Luigi Lorenzini Beatrice Orso David Vállez García Giuseppe Pontillo Silvia Ingala Sven Haller Kaj Blennow Giovanni B Frisoni Gael Chetelat Pierre Payoux Pablo Martinez-Lage Michael Ewers Adam Waldman Craig W. Ritchie Juan Domingo Gispert Pieter Jelle Visser Henri JMM Mutsaerts Betty M. Tijms Alle Meije Wink Frederik Barkhof Department of Radiology and Nuclear Medicine Amsterdam Neuroscience Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam Netherlands Department of Radiology and Nuclear Medicine Amsterdam UMC Location VuMC Vijre Universiteit Amsterdam Amsterdam Netherlands Department of Neuroscience Rehabilitation Ophthalmology Genetics Maternal and Child Health (DINOGMI) University of Genoa Genova Italy Department of Radiology & Nuclear Medicine Amsterdam UMC location VUmc Amsterdam Netherlands Department of Advanced Biomedical Sciences University “Federico II” Naples Italy Department of Electrical Engineering and Information Technology University of Naples “Federico II” Naples Italy University of Geneva Faculty of Medicine Geneva Switzerland Uppsala University Uppsala Sweden Department of Psychiatry and Neurochemistry The Sahlgrenska Academy at the University of Gothenburg Mölndal Sweden Clinical Neurochemistry Laboratory Sahlgrenska University Hospital Mölndal Sweden IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli Brescia Italy Geneva Memory Center Department of Rehabilitation and Geriatrics Geneva University Hospitals Geneva Switzerland ToNIC Toulouse NeuroImaging Center Université de Toulouse Inserm UPS Toulouse France INSERM Imagerie cérébrale et handicaps neurologiques Toulouse France CITA-Alzheimer Foundation San Sebastian Spain German Center for Neurodegenerative Diseases (DZNE) Munich Germany Imperial College London London United Kingdom Centre for Clinical Brain Sciences The University of Edinburgh Edinburgh United Kingdom Centre for Dementia Prevention at the University of Edinburgh Edinburgh United Kingdom Centro de Investigación Biomédica en Red Bioingeniería Biomateriales y Nanomedicina Madrid Spain Barcelonaβeta Brain Research Center (BBRC) Pasqual Maragall Foundation Barcelona Spain IMIM (Hospital del Mar Medical Research Institute) Barcelona Spain Universitat Pompeu Fabra Barcelona Spain Alzheimer Centrum Limburg Maastricht Univ

Background Early amyloid deposition results in functional and structural brain alterations in predementia stages of Alzheimer's disease (AD). However, how early functional and structural brain changes are related to each other remains unclear. Investigating the simultaneous disruptions of functional-structural brain features within individuals in relation to amyloid deposition may increase our understanding of the neuropathological processes underlying AD. Method We included 648 non-demented participants from the European Prevention for Alzheimer’s Dementia (EPAD) cohort vIMI baseline data release. The cut-off for cerebrospinal fluid (CSF) amyloid-β positivity (A+) was defined at <1000pg/mL (Elecsys assay). Functional connectivity was estimated with functional eigenvector centrality (EC) from the resting state functional MRI. White matter (WM) integrity was estimated with fractional anisotropy (FA), computed on diffusion MRI. Both measures were computed at the voxel level within the gray and white matter, respectively. First, we used linear regression models to investigate differences between A+ and A- participants within each modality separately, adjusting for age, sex, and site. In the whole group, we performed sparse canonical correlation analysis (sCCA) on functional and structural measures, identifying shared components between the two modalities. Individual sCCA scores were compared between amyloid groups using a generalized linear model (GLM). To evaluate this association in A+ individuals, the same sCCA analysis was performed normalizing the structural and functional matrices using the mean and SD from A- participants. Results Sample characteristics are provided in Table 1 . Differences in voxelwise EC and FA between amyloid groups are shown in Figure 1 . In the whole group, sCCA analysis showed that EC in the default mode, executive and cerebellar networks covary with FA in parietal, temporal and cerebellar areas ( Figure 2 ). sCCA projections were differen

关键词：

The Liver Tumor Segmentation Benchmark (LiTS)

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arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image computing and computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

Author Correction: Federated learning enables big data for rare cancer boundary detection

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Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

Identification of pre-microRNAs by characterizing their sequence order evolution information and secondary structure graphs

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BMC bioinformatics 2018年第SUPPL 19期19卷 521页

作者： Yuanlin Ma Zuguo Yu Guosheng Han Jinyan Li Vo Anh Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Hunan 411105 China. Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Hunan 411105 China. yuzuguo@***. School of Electrical Engineering and Computer Science Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia. yuzuguo@***. Advanced Analytics Institute Faculty of Engineering & IT University of Technology Sydney P.O Box 123 Broadway NSW 2007 Australia. School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane Q4001 Australia.

BACKGROUND:Distinction between pre-microRNAs (precursor microRNAs) and length-similar pseudo pre-microRNAs can reveal more about the regulatory mechanism of RNA biological processes. Machine learning techniques have been widely applied to deal with this challenging problem. However, most of them mainly focus on secondary structure information of pre-microRNAs, while ignoring sequence-order information and sequence evolution information. RESULTS:We use new features for the machine learning algorithms to improve the classification performance by characterizing both sequence order evolution information and secondary structure graphs. We developed three steps to extract these features of pre-microRNAs. We first extract features from PSI-BLAST profiles and Hilbert-Huang transforms, which contain rich sequence evolution information and sequence-order information respectively. We then obtain properties of small molecular networks of pre-microRNAs, which contain refined secondary structure information. These structural features are carefully generated so that they can depict both global and local characteristics of pre-microRNAs. In total, our feature space covers 591 features. The maximum relevance and minimum redundancy (mRMR) feature selection method is adopted before support vector machine (SVM) is applied as our classifier. The constructed classification model is named MicroRNA -NHPred. The performance of MicroRNA -NHPred is high and stable, which is better than that of those state-of-the-art methods, achieving an accuracy of up to 94.83% on same benchmark datasets. CONCLUSIONS:The high prediction accuracy achieved by our proposed method is attributed to the design of a comprehensive feature set on the sequences and secondary structures, which are capable of characterizing the sequence evolution information and sequence-order information, and global and local information of pre-microRNAs secondary structures. MicroRNA -NHPred is a valuable method for pre-microRNAs iden

关键词： Hibert-Huang transform Network PSI-BLAST profiles Pre-microRNA SVM mRMR

Index modulation for 5G: Striving to do more with less

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arXiv 2017年

作者： Cheng, Xiang Zhang, Meng Wen, Miaowen Yang, Liuqing State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics and Computer Science Peking University Beijing100871 China State Key Laboratory of Integrated Services Networks Xidian University Xi'an710000 China China Telecom Technology Innovation Center Beijing102209 China School of Electronic and Information Engineering South China University of Technology Guangzhou510640 China Department of Electrical and Computer Engineering Colorado State University Fort CollinsCO80523 United States

The fifth generation (5G) wireless communications brag both high spectrum efficiency and high energy efficiency. To meet the requirements, various new techniques have been proposed. Among these, the recently-emerging index modulation has attracted significant interests. By judiciously activating a subset of certain communication building blocks, such as antenna, subcarrier and time slot, index modulation is claimed to have the potential to meet the challenging 5G needs. In this article, we will discuss index modulation and its general and specific representations, enhancements, and potential applications in various 5G scenarios. The objective is to reveal whether, and how, index modulation may strive for more performance gains with less medium resource occupation. Copyright © 2017, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions

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arXiv 2018年

作者： Maier-Hein, Lena Eisenmann, Matthias Reinke, Annika Onogur, Sinan Stankovic, Marko Scholz, Patrick Arbel, Tal Bogunovic, Hrvoje Bradley, Andrew P. Carass, Aaron Carolin Feldmann Frangi, Alejandro F. Ful, Peter M. van Ginneken, Bram Hanbury, Allan Honauer, Katrin Kozubek, Michal Landman, Bennett A. Marz, Keno Maier, Oskar Maier-Hein, Klaus Menze, Bjoern H. Müller, Henning Neher, Peter F. Niessen, Wiro Rajpoot, Nasir Sharp, Gregory C. Sirinukunwattana, Korsuk Speidel, Stefanie Stock, Christian Stoyanov, Danail Taha, Abdel Aziz van der Sommen, Fons Wang, Ching-Wei Weber, Marc-André Zheng, Guoyan Jannin, Pierre Kopp-Schneider, Annette Heidelberg Germany Centre for Intelligent Machines McGill University MontrealQC Canada Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna Vienna Austria Science and Engineering Faculty Queensland University of Technology BrisbaneQLD Australia Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore United States CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine University of Sheffield Sheffield United Kingdom Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center Nijmegen Netherlands Institute of Information Systems Engineering TU Wien Vienna Austria Heidelberg University Heidelberg Germany Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Electrical Engineering Vanderbilt University NashvilleTN United States Institute of Medical Informatics Universität zu Lübeck Lübeck Germany Heidelberg Germany Institute for Advanced Studies Department of Informatics Technical University of Munich Munich Germany Information System Institute HES-SO Sierre Switzerland Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC Rotterdam Netherlands Department of Computer Science University of Warwick Coventry United Kingdom Department of Radiation Oncology Massachusetts General Hospital BostonMA United States Institute of Biomedical Engineering University of Oxford Oxford United Kingdom National Center for Tumor Diseases Dresden Dresden Germany Heidelberg Germany Department of Computer Science University College London London United Kingdom Data Science Studio Research Studios Austria FG Vienna Austria Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands AIExplore NTUST Center of Computer Vision and Medical Imaging Graduate Institute of Biomedical Engineering National Ta

International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now. We demonstrate the importance of challenges and show that the lack of quality control has critical consequences. First, reproducibility and interpretation of the results is often hampered as only a fraction of relevant information is typically provided. Second, the rank of an algorithm is generally not robust to a number of variables such as the test data used for validation, the ranking scheme applied and the observers that make the reference annotations. To overcome these problems, we recommend best practice guidelines and define open research questions to be addressed in the future. Copyright © 2018, The Authors. All rights reserved.

关键词： Quality control