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Annual Conference of Industrial Electronics Society

作者： Yunong Zhang Ziyu Yin Huanchang Huang Liangyu He Long Jin Sun Yat-Sen University Guangzhou Guangdong CN Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China SYSU-CMU Shunde International Joint Research Institute Foshan China School of Information Science and Technology Sun Yat-Sen University (SYSU) Guangzhou China

In this paper, to achieve the fault-tolerant capability for redundant manipulators, a dimension-reduction method is presented and investigated at the joint-acceleration level. By incorporating such a dimension-reduction method and the limits of joint angle, joint velocity as well as joint acceleration (i.e., the physical constraints on joints), an acceleration fault-tolerant scheme for redundant manipulator motion planning and control (or say, motion-planning-and-control, MPaC) is thus proposed and investigated. The scheme is then reformulated as a quadratic program (QP) subject to equality and bound constraints. For the online solution of the proposed scheme, the PLPE (piecewise-linear projection equation) oriented numerical algorithm is adopted to obtain the final QP solver. The derived QP resulting from the proposed scheme combines the abilities of fault tolerance, joints limits avoidance and repetitive motion, which can achieve the repetitive motion before and after the fault tolerance.

关键词： Fault tolerance Fault tolerant systems Manipulators Acceleration Planning

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Toward multidiversified ensemble clustering of high-dimensional data: From subspaces to metrics and beyond

arXiv

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arXiv 2017年

作者： Huang, Dong Wang, Chang-Dong Lai, Jian-Huang Kwoh, Chee-Keong The College of Mathematics and Informatics South China Agricultural University Guangzhou China Pazhou Lab Guangzhou China The School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China The School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

The rapid emergence of high-dimensional data in various areas has brought new challenges to current ensemble clustering research. To deal with the curse of dimensionality, recently considerable efforts in ensemble clustering have been made by means of different subspace-based techniques. However, besides the emphasis on subspaces, rather limited attention has been paid to the potential diversity in similarity/dissimilarity metrics. It remains a surprisingly open problem in ensemble clustering how to create and aggregate a large population of diversified metrics, and furthermore, how to jointly investigate the multi-level diversity in the large populations of metrics, subspaces, and clusters in a unified framework. To tackle this problem, this paper proposes a novel multidiversified ensemble clustering approach. In particular, we create a large number of diversified metrics by randomizing a scaled exponential similarity kernel, which are then coupled with random subspaces to form a large set of metric-subspace pairs. Based on the similarity matrices derived from these metric-subspace pairs, an ensemble of diversified base clusterings can thereby be constructed. Further, an entropy-based criterion is utilized to explore the cluster-wise diversity in ensembles, based on which three specific ensemble clustering algorithms are presented by incorporating three types of consensus functions. Extensive experiments are conducted on 30 high-dimensional datasets, including 18 cancer gene expression datasets and 12 image/speech datasets, which demonstrate the superiority of our algorithms over the state-of-the-art. The source code is available at https://***/huangdonghere/MDEC. Copyright © 2017, The Authors. All rights reserved.

关键词： Cluster analysis

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Topology analysis system for vehicular Ad Hoc network 17

Topology analysis system for vehicular Ad Hoc network

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17th International Conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2016

作者： Dong, Baihong Deng, Jian Wu, Weigang Meng, Tianyu 1. School of Data and Computer Science Sun Yat-Sen Univ. China Guangdong Province Key Lab. of Big Data Analysis and Processing China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (纸本)9781509050819

With the development of the technology, Vehicular Ad-hoc Network is developing rapidly and continuously. And many new algorithms in VANET were put forward. For example, many people apply the Named Data Networks or Software Defined Networking6 to the VANET. However, few researches are looking into the analysis of the topology of the VANET, among most of which are based on simple situations and calculation of the track of vehicles. And therefore, these analysis are inadequate for analyzing the topology under complex situations. This paper is going to put forward a new method for analyzing the change of the topology of VANET, which is capable to quantitatively analyze the change under different and complex situations. In the real experiment, when compared with traditional analysis methods, this new method can analyze more precisely the impact of some possible wireless communication problem on the change of the topology, for example, the impact of hidden node on the vehicular wireless communication and whether the number or the density of vehicles makes the topology of VANET more stable. And this new method can provide a numerical result instead of purely qualitative analysis. © 2016 IEEE.

关键词： Topology

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A unified leader-follower scheme for mobile robots with uncalibrated on-board camera

A unified leader-follower scheme for mobile robots with unca...

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2017 IEEE International Conference on Robotics and Automation, ICRA 2017

作者： Guo, Dejun Wang, Hesheng Chen, Weidong Liu, Ming Xia, Zeyang Leang, Kam K. Lab University of Utah Robotics Center Salt Lake CityUT84112 United States Department of Automation Shanghai Jiao Tong University Shanghai200240 China Department of Electronics and Computer Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong Institute of Biomedical and Health Engineering Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9781509046331

This paper studies the problem of image-based leader-follower formation control for mobile robots, where the controller is designed independently of the leader's motion. An adaptive control scheme, which is suitable for both omnidirectional and perspective cameras, is proposed. The proposed approach avoids the need for accurate calibration of the extrinsic parameters of the omnidirectional camera as well as the intrinsic and extrinsic parameters of perspective camera. Additionally, the coefficients of the plane where the feature point moves relative to the camera frame can be uncertain. These uncertain constant parameters are estimated using an adaptive estimator. Uniform Semi-global Practical Asymptotic Stability (USPAS) of the system is shown using the Lyapunov approach. Experimental results are presented to demonstrate the effectiveness of the proposed control scheme. © 2017 IEEE.

关键词： Mobile robots

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Person re-identification by camera correlation aware feature augmentation

arXiv

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arXiv 2017年

作者： Chen, Ying-Cong Zhu, Xiatian Zheng, Wei-Shi Lai, Jian-Huang School of Data and Computer Science Sun Yat-sen University Guangzhou510275 China Collaborative Innovation Center of High Performance Computing National University of Defense Technology Changsha410073 China Department of Computer Science and Engineering Chinese University of Hong Kong School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Key Laboratory of Machine Intelligence and Advanced Computing Sun Yatsen University Ministry of Education China Guangdong Province Key Laboratory of Information Security China

The challenge of person re-identification (re-id) is to match individual images of the same person captured by different non-overlapping camera views against significant and unknown cross-view feature distortion. While a large number of distance metric/subspace learning models have been developed for re-id, the cross-view transformations they learned are view-generic and thus potentially less effective in quantifying the feature distortion inherent to each camera view. Learning view-specific feature transformations for re-id (i.e., view-specific re-id), an under-studied approach, becomes an alternative resort for this problem. In this work, we formulate a novel view-specific person re-identification framework from the feature augmentation point of view, called Camera coRrelation Aware Feature augmenTation (CRAFT). Specifically, CRAFT performs cross-view adaptation by automatically measuring camera correlation from cross-view visual data distribution and adaptively conducting feature augmentation to transform the original features into a new adaptive space. Through our augmentation framework, view-generic learning algorithms can be readily generalized to learn and optimize view-specific sub-models whilst simultaneously modelling view-generic discrimination information. Therefore, our framework not only inherits the strength of view-generic model learning but also provides an effective way to take into account view specific characteristics. Our CRAFT framework can be extended to jointly learn view-specific feature transformations for person re-id across a large network with more than two cameras, a largely under-investigated but realistic re-id setting. Additionally, we present a domain-generic deep person appearance representation which is designed particularly to be towards view invariant for facilitating cross-view adaptation by CRAFT. We conducted extensively comparative experiments to validate the superiority and advantages of our proposed framework over state-of

关键词： Cameras

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Search for D0 meson decays to π+π−e+e− and K+K−e+e− final states

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Physical Review D 2025年第9期111卷 L091101-L091101页

作者： R. Aaij A. S. W. Abdelmotteleb C. Abellan Beteta F. Abudinén T. Ackernley A. A. Adefisoye B. Adeva M. Adinolfi P. Adlarson C. Agapopoulou C. A. Aidala Z. Ajaltouni S. Akar K. Akiba P. Albicocco J. Albrecht F. Alessio M. Alexander Z. Aliouche P. Alvarez Cartelle R. Amalric S. Amato J. L. Amey Y. Amhis L. An L. Anderlini M. Andersson A. Andreianov P. Andreola M. Andreotti D. Andreou A. Anelli D. Ao F. Archilli M. Argenton S. Arguedas Cuendis A. Artamonov M. Artuso E. Aslanides R. Ataíde Da Silva M. Atzeni B. Audurier D. Bacher I. Bachiller Perea S. Bachmann M. Bachmayer J. J. Back P. Baladron Rodriguez V. Balagura A. Balboni W. Baldini L. Balzani H. Bao J. Baptista de Souza Leite C. Barbero Pretel M. Barbetti I. R. Barbosa R. J. Barlow M. Barnyakov S. Barsuk W. Barter J. Bartz J. M. Basels S. Bashir G. Bassi B. Batsukh P. B. Battista A. Bay A. Beck M. Becker F. Bedeschi I. B. Bediaga N. A. Behling S. Belin K. Belous I. Belov I. Belyaev G. Benane G. Bencivenni E. Ben-Haim A. Berezhnoy R. Bernet S. Bernet Andres A. Bertolin C. Betancourt F. Betti J. Bex Ia. Bezshyiko J. Bhom M. S. Bieker N. V. Biesuz P. Billoir A. Biolchini M. Birch F. C. R. Bishop A. Bitadze A. Bizzeti T. Blake F. Blanc J. E. Blank S. Blusk V. Bocharnikov J. A. Boelhauve O. Boente Garcia T. Boettcher A. Bohare A. Boldyrev C. S. Bolognani R. Bolzonella R. B. Bonacci N. Bondar A. Bordelius F. Borgato S. Borghi M. Borsato J. T. Borsuk E. Bottalico S. A. Bouchiba M. Bovill T. J. V. Bowcock A. Boyer C. Bozzi J. D. Brandenburg A. Brea Rodriguez N. Breer J. Brodzicka A. Brossa Gonzalo J. Brown D. Brundu E. Buchanan L. Buonincontri M. Burgos Marcos A. T. Burke C. Burr J. S. Butter J. Buytaert W. Byczynski S. Cadeddu H. Cai A. C. Caillet R. Calabrese S. Calderon Ramirez L. Calefice S. Cali M. Calvi M. Calvo Gomez P. Camargo Magalhaes J. I. Cambon Bouzas P. Campana D. H. Campora Perez A. F. Campoverde Quezada S. Capelli L. Capriotti R. Caravaca-Mora A. Carbone L. Carcedo Salgado R. Cardinale A. Cardini P. Carniti L. Carus A. Casais Vidal R. Caspary G. Casse M Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Department of Physics University of Warwick Coventry United Kingdom Physik-Institut Universität Zürich Zürich Switzerland Oliver Lodge Laboratory University of Liverpool Liverpool United Kingdom Syracuse University Syracuse New York USA Instituto Galego de Física de Altas Enerxías (IGFAE) Universidade de Santiago de Compostela Santiago de Compostela Spain H.H. Wills Physics Laboratory University of Bristol Bristol United Kingdom Department of Physics and Astronomy Uppsala University Uppsala Sweden (associated with School of Physics and Astronomy University of Glasgow Glasgow United Kingdom) Université Paris-Saclay CNRS/IN2P3 IJCLab Orsay France University of Michigan Ann Arbor Michigan USA (associated with Syracuse University Syracuse New York USA) Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France INFN Laboratori Nazionali di Frascati Frascati Italy Fakultät Physik Technische Universität Dortmund Dortmund Germany Also at Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany. European Organization for Nuclear Research (CERN) Geneva Switzerland School of Physics and Astronomy University of Glasgow Glasgow United Kingdom Department of Physics and Astronomy University of Manchester Manchester United Kingdom Cavendish Laboratory University of Cambridge Cambridge United Kingdom LPNHE Sorbonne Université Paris Diderot Sorbonne Paris Cité CNRS/IN2P3 Paris France Universidade Federal do Rio de Janeiro (UFRJ) Rio de Janeiro Brazil School of Physics State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China INFN Sezione di Firenze Firenze Italy Authors affiliated with an institute formerly covered by a cooperation agreement with CERN INFN Sezione di Ferrara Ferrara Italy INFN Sezione di Milano-Bicocca Milano Italy Also at Università degli Studi di Milano-Bicocca Milano Italy. University of Chinese Academy o

A search for D0 meson decays to the π+π−e+e− and K+K−e+e− final states is reported using a sample of proton-proton collisions collected by the LHCb experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 6 fb−1. The decay D0→π+π−e+e− is observed for the first time when requiring that the two electrons are consistent with coming from the decay of a ϕ or ρ0/ω meson. The corresponding branching fractions are measured relative to the D0→K−π−[e+e−]ρ0/ω decay, where the two electrons are consistent with coming from the decay of a ρ0 or ω meson. No evidence is found for the D0→K+K−e+e− decay and world-best limits are set on its branching fraction. The results are compared to, and found to be consistent with, the branching fractions of the D0→π+π−μ+μ− and D0→K+K−μ+μ− decays recently measured by LHCb and confirm lepton universality at the current precision.

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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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INTELLIGIBLE ENHANCEMENT OF 3D ARTICULATION ANIMATION BY INCORPORATING AIRFLOW INFORMATION

INTELLIGIBLE ENHANCEMENT OF 3D ARTICULATION ANIMATION BY INC...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Fei Chen Hui Chen Lan Wang Ying Zhou Jiaying He Nan Yan Gang Peng Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology

ISBN: (纸本)9781479999897

The 3D talking head has been developed fast, in which both external and internal articulators were demonstrated. For Mandarin pronunciation, the aspiration airflow is crucial to discriminate confusable Mandarin consonants. In this paper, we present a 3D talking head system for articulatory and aspiration animation with the use of EMA articulation data and airflow data simultaneously. The quantitative analyses of airflow data indicated confusable Mandarin consonants could be distinguished from each other by the mean airflow during voicing, peak expiratory airflow, and airflow duration. An airflow model was then incorporated into the 3D articulatory model to produce the airflow in accordance with articulator movements of Mandarin pronunciation. An audio-visual test was designed to evaluate the current 3D articulation and aspiration system, where minimal pairs were used to recognize the animation. The identification accuracy was significantly improved from 43.9% without airflow to 84.8% with airflow-incorporated information.

关键词： 3D articulatory dynamics Airflow PAS confusable consonants intelligible enhancement

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Current Situation and Development of intelligence Robots

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ZTE Communications 2016年第B12期14卷 25-34页

作者： REN Fuji SUN Xiao Anhui Key Lab of Affective Computing and Advanced Intelligence Machine Hefei University of Technology Hefei 230009 China Tokushima University Tokushima 770800 Japan

Industrial intelligent robots are treated as a measure of na- tional scientific level and technology innovation, and also the important symbol of high-level manufacturing, while service intelligent robots can directly affect people＇ s daily lives. The development of artificial robots in different areas is at- tracting much attention around the world. This article re- views the current situation and development of Chinese and international intelligent robot markets including industrial ro- bots and service robots. The intelligent robot technology and the classification of robots are also discussed. Finally, appli- cations of intelligent robots in various fields are concluded and the development trends and outlook of intelligent robots are explored.

关键词： intelligent robot artificial intelligence development trends

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QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

arXiv

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arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

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