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arXiv 2017年

作者： Nan, Yang Coppola, Gianmarc Liang, Qiaokang Zou, Kunglin Sun, Wei Zhang, Dan Wang, Yaonan Yu, Guanzhen College of Electrical and Information Engineering Hunan University Changsha410082 China National Engineering Laboratory for Robot Vision Perception and Control Hunan University Changsha Hunan410082 China Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing Hunan418000 China Faculty of Engineering and Applied Science University of Ontario Institute of Technology OshawaONL1H 7K4 Canada Department of Mechanical Engineering York University TorontoONM3J 1P3 Canada Department of Oncology Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine Shanghai201203 China

Gastric cancer is the second leading cause of cancer‐related deaths worldwide, and the major hurdle in biomedical image analysis is the determination of the cancer extent. This assignment has high clinical relevance and would generally require vast microscopic assessment by pathologists. Recent advances in deep learning have produced inspiring results on biomedical image segmentation, while its outcome is reliant on comprehensive annotation. This requires plenty of labor costs, for the ground truth must be annotated meticulously by pathologists. In this paper, a reiterative learning framework was presented to train our network on partial annotated biomedical images, and superior performance was achieved without any pre‐trained or further manual annotation. We eliminate the boundary error of patch‐based model through our overlapped region forecast algorithm. Through these advisable methods, a mean intersection over union coefficient (IOU) of 0.883 and mean accuracy of 91.09% on the partial labeled dataset was achieved, which made us win the 2017 China Big Data & Artificial Intelligence Innovation and Entrepreneurship Competitions1. Copyright © 2017, The Authors. All rights reserved.

关键词： Diseases

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ACCELERATE CONVOLUTIONAL NEURAL NETWORKS FOR BINARY CLASSIFICATION VIA CASCADING COST-SENSITIVE FEATURE

ACCELERATE CONVOLUTIONAL NEURAL NETWORKS FOR BINARY CLASSIFI...

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IEEE International Conference on Image Processing

作者： Junbiao Pang Huihuang Lin Li Su Chunjie Zhang Weigang Zhang Lijuan Duan Qingming Huang Baocai Yin Beijing Key Laboratory of Multimedia and Intelligent Software Technology China School of Computer and Control Engineering University of Chinese Academy of Sciences China School of Computer Science and Technology Harbin Institute of Technology at Weihai China College of Computer Science and Technology Beijing University of Technology China

ISBN: (纸本)9781467399623

Convolutional Neural Networks (CNNs) have delivered impressive state-of-the-art performances for many vision tasks, while the computation costs of these networks during test-time are notorious. Empirical results have discovered that CNNs have learned the redundant representations both within and across different layers. When CNNs are applied for binary classification, we investigate a method to exploit this redundancy across layers, and construct a cascade of classifiers which explicitly balances classification accuracy and hierarchical feature extraction costs. Our method cost-sensitively selects feature points across several layers from trained networks and embeds non-expensive yet discriminative features into a cascade. Experiments on binary classification demonstrate that our framework leads to drastic test-time improvements, e.g., possible 47.2× speedup for TRECVID upper body detection, 2.82× speedup for Pascal VOC2007 People detection, 3.72× for INRIA Person detection with less than 0.5% drop in accuracies of the original networks.

关键词： Neural networks Convolution Acceleration Optimization Upper bound Computers Computer science

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Consensus in second-order multi-agent systems via impulsive control using position-only information with heterogeneous delays

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IET control Theory and Applications 2015年第3期9卷 336-345页

作者： Wang, Yan-Wu Yi, Jing-Wen School of Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science AndTechnology Ministry of China China

This study investigates the consensus problem of second-order multi-agent systems (MASs) via impulsive control using position-only information with communication delays. The communication delays between any two distinct agents are different which can be larger than one impulsive period. A distributed impulsive consensus protocol is designed, in which only the delayed sampled relative positions to neighbours and the relative position to the last sampling state are utilised. By introducing some virtual subgraphs and performing three steps of model transformation, the consensus problem of original continuous-time system is converted to the stability problem of a discrete-time expanded error system. Some necessary and sufficient criteria are derived to grantee the dynamic average-consensus of the MAS. Furthermore, a special case, that is, the delays less than one impulsive period, is discussed, and the bounds of impulsive periods for the dynamic average-consensus are obtained in explicit expressions for both the undirected and directed communication graphs, respectively. Two numerical simulation examples are given to illustrate the effectiveness of theoretical results. © 2015 The Institution of Engineering and technology.

关键词： Multi agent systems

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Synchronisation of complex dynamical networks with additive stochastic time-varying delays

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International Journal of Systems science 2016年第5期47卷 1221-1229页

作者： Yi, Jing-Wen Wang, Yan-Wu Xiao, Jiang-Wen Huang, Yuehua School of Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China College of Electrical Engineering and Information Science China Three Gorges University Yichang China

In this letter, a class of complex dynamical networks with additive stochastic time-varying delays is investigated. Two kinds of delays in complex dynamical networks are taken into consideration, one is called the nodes-induced delay in this paper, and the other is the network-induced delay. Both of the delays are assumed to obey different stochastic distributions. By utilising a novel Lyapunov-Krasovskii functional and stochastic analysis techniques, a sufficient criterion is obtained in the form of linear matrix inequalities to ensure the synchronisation of the complex dynamical networks. Finally, a numerical example is given to illustrate the feasibility and effectiveness of the proposed method. © 2014 Taylor & Francis.

关键词： Synchronization

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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

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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

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A Switching Approach to Designing Finite-Time Synchronization controllers of Coupled Neural Networks

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IEEE Transactions on Neural Networks and Learning Systems 2016年第2期27卷 471-482页

作者： Liu, Xiaoyang Su, Housheng Chen, Michael Z. Q. School of Computer Science and Technology Jiangsu Normal University Xuzhou221116 China Image Processing and Intelligent Control Key Laboratory School of Automation Huazhong University of Science and Technology Wuhan430074 China Department of Mechanical Engineering University of Hong Kong Hong Kong Hong Kong

This paper is concerned with the finite-Time synchronization issue of nonlinear coupled neural networks by designing a new switching pinning controller. For the fixed network topology and control strength, the newly d... 详细信息

关键词： Network topology

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An improved reference point sampling method on Pareto optimal front

An improved reference point sampling method on Pareto optima...

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Congress on Evolutionary Computation

作者： Cheng He Linqiang Pan Hang Xu Ye Tian Xingyi Zhang Laboratory of Image Information Processing and Intelligent Control Huazhong University of Science and Technology Wuhan Hubei China Huazhong University of Science and Technology Wuhan Hubei CN Department of Computer Science Xiamen University Xiamen Fujian China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei Anhui China

ISBN: (纸本)9781509006243

In this paper, we propose a sampling approach of reference points used for performance metrics of multi-objective evolutionary algorithms. Traditional reference point sampling methods, such as the Das and Dennis method, usually sample the reference points via a set of uniformly distributed weight vectors generated on an ideal hyper-plane in objective space, which however often ignore the geometric shape of a specific Pareto front. Therefore, we propose a novel reference point sampling approach by taking the specific shape of the Pareto optimal front to be tackled into account for measuring the performance of multi-objective evolutionary algorithms. The performance of the proposed reference point sampling method against the other two state-of-the-art sampling methods is tested on six test instances in various conditions, which clearly demonstrate the effectiveness and superiority of the proposed sampling method.

关键词： Sampling methods Shape Convergence Evolutionary computation Optimization Euclidean distance

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The Switching Condition of Guidance Laws for Compound control Missile 34

The Switching Condition of Guidance Laws for Compound Contro...

引用

第三十四届中国控制会议

作者： SUN Sheng LIU Xiaodong WEI Xiaolei LIU Hailiang YAO Yinwei BeiJing Aerospace Automatic Control Institute National Key Laboratory of Science and Technology on Aerospace Intelligent Control The Second Artillery Resident office in CASIC

ISBN: (纸本)9781467374439

Based on the linear time-varying relative motion equation and with consideration of the lateral acceleration limitation, the necessary boundary for controllable line-of-sight angular rate is obtained. With consideration of the dynamic time-delay property of missile autopilot, the necessary boundary for controllable line-of-sight angular rate of guidance system is analyzed. Moreover, the guidance law, which can guarantee the stability of guidance system with the condition of intercepting the high maneuvering target, is put forward. Based on the working properties of the compound control missile, the switching condition to compound control, which can save more energy, is designed subsequently. The simulation results show that the guidance precision of the proposed guidance scheme is better than the existing guidance approaches, and the working time for compound control is shorter.

关键词： Guidance Law,Compound control,Switching Condition,Time-Delay Property

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Image Blind Restoration Based on Blur Identification and Quality Assessment of Restored Image

Image Blind Restoration Based on Blur Identification and Qua...

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第三十四届中国控制会议

作者： YIN Lei DI Xiaoguang FU Shaowen GAO Lei MA Jie Control and Simulation Center Harbin Institute of Technology National Key Laboratory of Science and Technology on Aerospace Intelligent Control

ISBN: (纸本)9781467374439

Nowadays, most of image blind restoration algorithms suffer from the problem of being unreliable and too time-consuming due to the large amounts of iterations involved in the algorithms. Moreover, because of the artifacts induced by blind restoration process, the restored images have a worse quality than the original. All the above greatly limit the application of the existing image blind restoration algorithms to real-time video processing. To solve the problems, an improved image restoration process is proposed to reduce the image restoration time while maintaining the quality of restored images. First, a novel image blur identification index is constructed to evaluate the image sharpness. The image blur identification result will be used to determine whether the following procedures should be performed. Second, a normalized sparse regularization blind restoration algorithm is used to restore the image. At last, a novel no-reference image quality assessment algorithm with luminance, contrast, structure, sharpness and ringing metric is designed to evaluate the restoration result. Experiment results show that the proposed blur identification algorithm and the no-reference image quality assessment method are effective in improving the image restoration efficiency while ensuring a reliable output.

关键词： Blur Identification,Image Quality Assessment,Blind Restoration,Sparse Regularization,Ringing Metric

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Revisiting the pulsational characteristics of the exoplanet host star β pictoris

arXiv

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arXiv 2019年

作者： Zwintz, K. Reese, D.R. Neiner, C. Pigulski, A. Kuschnig, R. Müllner, M. Zieba, S. Abe, L. Guillot, T. Handler, G. Kenworthy, M. Stuik, R. Moffat, A.F.J. Popowicz, A. Rucinski, S.M. Wade, G.A. Weiss, W.W. Bailey, J.I. Crawford, S. Ireland, M. Lomberg, B. Mamajek, E.E. Mellon, S.N. Talens, G.J. Institut für Astro- und Teilchenphysik Universität Innsbruck Technikerstraße 25 InnsbruckA-6020 Austria LESIA Observatoire de Paris Université PSL CNRS Sorbonne Université Univ. Paris Diderot Sorbonne Paris Cité 5 place Jules Janssen Meudon92195 France Instytut Astronomiczny Uniwersytet Wroclawski Kopernika 11 Wroclaw51-622 Poland Institut für Kommunikationsnetze und Satellitenkommunikation Technical University Graz Inffeldgasse 12 GrazA-8010 Austria Université Côte d’Azur Observatoire de la Côte d’Azur CNRS Laboratoire Lagrange CS 34229 Nice Cedex 406304 France Nicolaus Copernicus Astronomical Center ul. Bartycka 18 Warsaw00-716 Poland Leiden Observatory Leiden University P.O. Box 9513 Leiden2300 RA Netherlands Département de physique Université de Montréal CP 6128 Succursale Centre-Ville MontréalQCH3C 3J7 Canada MontréalQCH3C 3J7 Canada Institute of Automatic Control Silesian University of Technology Akademicka 16 Gliwice Poland Department of Astronomy and Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada Department of Physics and Space Science Royal Military College of Canada PO Box 17000 Stn Forces KingstonONK7K 7B4 Canada Universität Wien Institut für Astrophysik Türkenschanzstrasse 17 WienA-1180 Austria Department of Physics University of California at Santa Barbara Santa BarbaraCA93111 United States Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States South African Astronomical Observatory Observatory Rd Observatory Cape Town Cape Town7700 South Africa Research School of Astronomy and Astrophysics Australian National University CanberraACT2611 Australia Department of Astronomy University of Cape Town Rondebosch Cape Town7700 South Africa Jet Propulsion Laboratory California Institute of Technology M/S 321-100 4800 Oak Grove Drive PasadenaCA91109 United States Department of Physics & Astronomy University of Rochester 500 Wilson Blvd. Ro

Context. Exoplanet properties crucially depend on their host stars’ parameters: more accurate stellar parameters yield more accurate exoplanet characteristics. In case the exoplanet host star shows pulsations, asteroseismology can be used for an improved description of the stellar parameters. Aims. We aim to revisit the pulsational properties of β Pic and identify its pulsation modes from normalised amplitudes in five different passbands. We also investigate the potential presence of a magnetic field. Methods. We conduct a frequency analysis using three seasons of BRITE-Constellation observations in the BRITE filters, the ∼620-day long bRing light curve and the nearly 8-year long SMEI photometric time series. We calculate normalised amplitudes using all passbands including previously published values obtained from ASTEP observations. We investigate the magnetic properties of β Pic using spectropolarimetric observations conducted with the HARPSpol instrument. Using 2D rotating models, we fit the normalised amplitudes and frequencies through Monte Carlo Markov Chains. Results. We identify 15 pulsation frequencies in the range from 34 to 55 d−1, where two – F13 at 53.6917 d−1 and F11 at 50.4921 d−1 – display clear amplitude variability. We use the normalised amplitudes in up to five passbands to identify the modes as three ` = 1, six ` = 2 and six ` = 3 modes. β Pic is shown to be non-magnetic with an upper limit of the possible undetected dipolar field of 300 Gauss. Conclusions. Multiple fits to the frequencies and normalised amplitudes are obtained including one with a near equator-on inclination for β Pic, which corresponds to our expectations based on the orbital inclination of β Pic b and the orientation of the circumstellar disk. This solution leads to a rotation rate of 27% of the Keplerian break-up velocity, a radius of 1.497±0.025 R , and a mass of 1.797±0.035 M . The ∼2% errors in radius and mass do not account for uncertainties in the models and a potentiall

关键词： Magnetic fields

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