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Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

Nature communications 2019年第1期10卷 588页

作者： Lena Maier-Hein Matthias Eisenmann Annika Reinke Sinan Onogur Marko Stankovic Patrick Scholz Tal Arbel Hrvoje Bogunovic Andrew P Bradley Aaron Carass Carolin Feldmann Alejandro F Frangi Peter M Full Bram van Ginneken Allan Hanbury Katrin Honauer Michal Kozubek Bennett A Landman Keno März Oskar Maier Klaus Maier-Hein Bjoern H Menze Henning Müller Peter F Neher Wiro Niessen Nasir Rajpoot Gregory C Sharp Korsuk Sirinukunwattana Stefanie Speidel Christian Stock Danail Stoyanov Abdel Aziz Taha Fons van der Sommen Ching-Wei Wang Marc-André Weber Guoyan Zheng Pierre Jannin Annette Kopp-Schneider Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. l.maier-hein@dkfz.de. Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Centre for Intelligent Machines McGill University Montreal QC H3A0G4 Canada. Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna 1090 Vienna Austria. Science and Engineering Faculty Queensland University of Technology Brisbane QLD 4001 Australia. Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore MD 21218 USA. CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine The University of Leeds Leeds Yorkshire LS2 9JT UK. Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center 6525 GA Nijmegen The Netherlands. Institute of Information Systems Engineering TU Wien 1040 Vienna Austria. Complexity Science Hub Vienna 1080 Vienna Austria. Heidelberg Collaboratory for Image Processing (HCI Heidelberg University 69120 Heidelberg Germany. Centre for Biomedical Image Analysis Masaryk University 60200 Brno Czech Republic. Electrical Engineering Vanderbilt University Nashville TN 37235-1679 USA. Institute of Medical Informatics Universität zu Lübeck 23562 Lübeck Germany. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Institute for Advanced Studies Department of Informatics Technical University of Munich 80333 Munich Germany. Information System Institute HES-SO Sierre 3960 Switzerland. Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC 3015 GD Rotterdam The Netherlands. Department of Computer Science University of Warwick Coventry CV4 7AL UK. Department of Radiation Oncology Massachusetts General Hospital Boston MA

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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An Improved Precoding of Approximative Matrix Inverse Computations Based on Norm Minimization Algorithm in Massive MIMO System

An Improved Precoding of Approximative Matrix Inverse Comput...

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2015 IEEE International Conference on Progress in Informatics and computing(PIC 2015)

作者： Hongliu Tu Yanjun Hu Yaohua Xu Fengrong Li Key laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information TechnologyChinese Academy of Sciences

In massive multiple-input multiple-output（MIMO） system, scaling up the antennas of base station（BS） has a clear benefit on sum rate and energy efficiency, but the signal processing complexity can be very high and many algorithms cannot be implemented in practice for high hardware cost. Approximati ve Matrix Inverse Computations（AMIC） algorithm is a kind of lowcomplexity precoding for large multiuser MIMO systems, but the Bite Error Rate（BER） performance is shown to be not better than the classical MMSE precoding. To improve the BER performance of AMIC algorithm, in this paper, we use norm minimization algorithm to change the coefficient of the precoding matrix to improve the BER performance of AMIC algorithm. It can verify that the proposed algorithm can achieve better BER performance than the AMIC algorithm by using only a limited number of Neumann series iterations, and keep lower complexity. The proposed scheme is a compromise solution between complexity and BER performance.

关键词： Massive MIMO linear precoding algorithm approximative matrix inverse norm minimization

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Fast assignment reduction in inconsistent incomplete decision systems

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Journal of Systems Engineering and Electronics 2014年第1期25卷 83-94页

作者： Min Li Shaobo Deng Shengzhong Feng Jianping Fan Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Nanchang Institute of Technology Graduate School of Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences

This paper focuses on fast algorithm for computing the assignment reduct in inconsistent incomplete decision systems. It is quite inconvenient to judge the assignment reduct directly ac-cording to its definition. We propose the judgment theorem for the assignment reduct in the inconsistent incomplete decision system, which greatly simplifies judging this type reduct. On such basis, we derive a novel attribute significance measure and construct the fast assignment reduction algorithm （F-ARA）, intended for com-puting the assignment reduct in inconsistent incomplete decision systems. Final y, we make a comparison between F-ARA and the discernibility matrix-based method by experiments on 13 Univer-sity of California at Irvine （UCI） datasets, and the experimental results prove that F-ARA is efficient and feasible.

关键词： assignment reduction upper approximation reduction,inconsistent incomplete decision system rough set.

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A sound and complete R-calculi with respect to contraction and minimal change

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Frontiers of Computer Science 2014年第2期8卷 184-191页

作者： Wei LI Yuefei SUI State Key Laboratory of Software Development Environment Beihang University Beijing 100191 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

AGM postulates are for belief revision （revision by a single belief）, and DP postulates are for iterated revision （revision by a finite sequence of beliefs）. R-calculus is given for R-configurations △｜Г, where △ is a set of atomic formulas or the negations of atomic formulas, and Г is a finite set of formulas. We shall give two R-calculi C and M （sets of de- duction rules） such that for any finite consistent sets Г, △of formulas in the propositional logic, there is a consistent set ⊙ Г C of formulas such that △IГ → △, ⊙ is provable and⊙ is a contraction of F by A or a minimal change of F by A; and prove that C and M are sound and complete with respect to the contraction and the minimal change, respectively.

关键词： belief revision R-calculus minimal change,contraction

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Differently implicational hierarchical inference algorithm under interval-valued fuzzy environment

Differently implicational hierarchical inference algorithm u...

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IEEE International Conference on Fuzzy Systems

作者： Yiming Tang Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Hefei 230009 P. R. China

ISBN: (纸本)9781467374293

Under interval-valued fuzzy environment, based on the differently implicational idea and hierarchical inference mechanism, the interval-valued universal triple I algorithm is put forward. To begin with, the interval-valued fuzzy implications and related residual pairs are researched. Furthermore, the interval-valued universal triple I principles are proposed, and the optimal solutions of the interval-valued universal triple I algorithm are achieved from the viewpoints of residual pairs together with the R-implications, and the corresponding hierarchical inference mode is established, meanwhile the reversible properties of the interval-valued universal triple I algorithm are proved. Finally, it is verified by examples that the interval-valued universal triple I algorithm performs better than the interval-valued triple I algorithm.

关键词： differently implicational idea hierarchical inference mechanism interval-valued universal

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Prediction of negative conversion days of childhood nephrotic syndrome based on the improved backpropagation neural network with momentum

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International Journal Bioautomation 2015年第4期19卷 543-554页

作者： Liu, Yijun Wang, Beihong Tang, Jiali Zhu, Mingfang Chen, Dan Jiang, Hongfen Chen, Xiangjun Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology Zhongwu Road 1801 Changzhou213001 China Department of Pediatrics Changzhou No.2 People's Hospital Xinglong Road 29 Changzhou213003 China Guangxi Higher Education Key Laboratory of Science Computing and Intelligent Information Processing Guangxi Teachers Education University Yanziling Road 4 Nanning530023 China

Childhood nephrotic syndrome is a chronic disease harmful to growth of children. Scientific and accurate prediction of negative conversion days for children with nephrotic syndrome offers potential benefits for treatment of patients and helps achieve better cure effect. In this study, the improved backpropagation neural network with momentum is used for prediction. Momentum speeds up convergence and maintains the generalization performance of the neural network, and therefore overcomes weaknesses of the standard backpropagation algorithm. The three-tier network structure is constructed. Eight indicators including age, lgG, lgA and lgM, etc. are selected for network inputs. The scientific computing software of MATLAB and its neural network tools are used to create model and predict. The training sample of twenty-eight cases is used to train the neural network. The test sample of six typical cases belonging to six different age groups respectively is used to test the predictive model. The low mean absolute error of predictive results is achieved at 0.83. The experimental results of the small-size sample show that the proposed approach is to some degree applicable for the prediction of negative conversion days of childhood nephrotic syndrome.

关键词： Momentum

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OBJECT SEGMENTATION WITH DEEP REGRESSION

OBJECT SEGMENTATION WITH DEEP REGRESSION

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IEEE International Conference on Image Processing

作者： Jianchao Li Dan Wang Canxiang Yan Shiguang Shan Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS University of Chinese Academy of Sciences Institute of Deep Learning Baidu Inc.

ISBN: (纸本)9781479983407

Object segmentation has constantly received much attention due to its fundamental role in scene understanding. Traditional methods formulate it as a structured prediction problem, represented by graphical models (GMs). However, most GMs have difficulties in balancing the effectiveness of context modeling and efficiency of model inference. In this paper, we model the contexts implicitly using the deep convolutional neural network (DCNN). Specifically, we reformulate object segmentation as a regression problem and train a deep network end-to-end to learn the nonlinear mapping from the image to the object mask. The large receptive field of the network incorporates wide contexts to update the network parameters, giving an implicit context model. Moreover, the deep architecture is favorable for modeling nonlinearity. The inference of our method is quite efficient, involving only a simple feed-forward pass. Extensive experiments on public datasets demonstrate the advantages of our method.

关键词： Object segmentation Context modeling Deep convolutional neural networks Regression Object segmentation Context modeling Regression (Psychology) network parameters Regression Regression

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Fusing PLSA model and Markov random fields for automatic image annotation

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High technology Letters 2014年第4期20卷 409-414页

作者：田东平 Zhao Xiaofei Shi Zhongzhi Institute of Computer Software Baoji University of Arts and Sciences Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences

A novel image auto-annotation method is presented based on probabilistic latent semantic analysis(PLSA) model and multiple Markov random fields(MRF).A PLSA model with asymmetric modalities is first constructed to estimate the joint probability between images and semantic concepts,then a subgraph is extracted served as the corresponding structure of Markov random fields and inference over it is performed by the iterative conditional modes so as to capture the final annotation for the *** novelty of our method mainly lies in two aspects:exploiting PLSA to estimate the joint probability between images and semantic concepts as well as multiple MRF to further explore the semantic context among keywords for accurate image *** demonstrate the effectiveness of this approach,an experiment on the Corel5 k dataset is conducted and its results are compared favorably with the current state-of-the-art approaches.

关键词： automatic image annotation probabilistic latent semantic analysis （PLSA） expectation maximization Markov random fields （MRF） image retrieval

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An Interrupt Scheduling Mechanism of Virtual Network Interface Card for Xen-Based System

An Interrupt Scheduling Mechanism of Virtual Network Interfa...

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2015 5th International Conference on Computer Sciences and Automation Engineering(ICCSAE 2015)

作者： Liuzheng Lu Runze Wan Jinrong He School of Computer Hubei University of Education Co-Innovation Centre of Elementary Education Information Technology Services of Hubei Science Computing and Intelligent Information Processing of Guangxi Higher Education Key Laboratory Guangxi Teachers Education University

As one of typical paravirtualization hypervisors, Xen has received widespread attentions especially its scaling capability under some kinds of workload. In this paper, we focus on the problem that the most CPU resources are occupied by frequent interrupt from the NIC（Network Interface Card） and it will cause bottleneck of the system for Xen. To alleviate this problem, this paper proposes an adaptive interrupt latency scheduling mechanism based on XEN, which use the polling or interrupt method in accordance with the queue length of virtual buffer without supplementing any additional processing unit. Also, the method can guarantee different quality of service to some extent by means of the definition of the two types of priority virtual buffers. Simulation results show that the mechanism can reduce CPU overhead significantly and improve system performance effectively.

关键词： virtualization interrupt latency virtual buffer

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