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Classifying Uncertain and Evolving Data Streams with Distributed Extreme Learning Machine

Journal of Computer Science & Technology 2015年第4期30卷 874-887页

作者：韩东红张昕王国仁 College of Information Science and Engineering Northeastern University Shenyang 110819 China Key Laboratory of Medical Image Computing ( NEU) Ministry of Education Shenyang 110819 China

Conventional classification algorithms are not well suited for the inherent uncertainty, potential concept drift, volume, and velocity of streaming data. Specialized algorithms are needed to obtain efficient and accurate classifiers for uncertain data streams. In this paper, we first introduce Distributed Extreme Learning Machine （DELM）, an optimization of ELM for large matrix operations over large datasets. We then present Weighted Ensemble Classifier Based on Distributed ELM （WE-DELM）, an online and one-pass algorithm for efficiently classifying uncertain streaming data with concept drift. A probability world model is built to transform uncertain streaming data into certain streaming data. Base classifiers are learned using DELM. The weights of the base classifiers are updated dynamically according to classification results. WE-DELM improves both the efficiency in learning the model and the accuracy in performing classification. Experimental results show that WE-DELM achieves better performance on different evaluation criteria, including efficiency, accuracy, and speedup.

关键词： uncertain data stream classification extreme learning machine distributed computing concept drift

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Semantic movie summarization based on string of IE-Role Nets

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Computational Visual Media 2015年第2期1卷 129-141页

作者： Wen Qu Yifei Zhang Daling Wang Shi Feng Ge Yu School of Information Science and Engineering Northeastern University Key Laboratory of Medical Image Computing(Northeastern University) Ministry of Education

Roles, their emotion, and interactions between them are three key elements for semantic content understanding of movies. In this paper, we proposed a novel movie summarization method to capture the semantic content in movies based on a string of IE-Role Nets. An IE-Role Net(interaction and emotion rolenet) models the emotion and interactions of roles in a shot of the movie. The whole movie is represented as a string of IE-Role Nets. Summarization of a movie is transformed into finding an optimal substring with user-specified summarization *** substring mining is conducted to find an optimal substring of the whole movie. We have conducted objective and subjective experiments on our method. Experimental results show the ability of our method to capture the semantic content of movies.

关键词： movie summarization content analysis movie understanding

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Noninvasive estimation of pressure difference of left and right ventricle based on young - Laplace model

Noninvasive estimation of pressure difference of left and ri...

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2016 IEEE International Conference on Information and Automation, IEEE ICIA 2016

作者： Kang, Hongyu Liu, Jun Xu, Lisheng Lv, Wenlong Qian, Wei Yang, Benqiang Zou, Mingyu Sino-Dutch Biomedical and Information Engineering School Northeastern University Shenyang Liaoning Province China Key Laboratory of Medical Image Computing Ministry of Education China School of Sciences Northeastern University Shenyang Liaoning Province China General hospital of Shenyang Military Shenyang Liaoning Province China

ISBN: (纸本)9781509041022

The paper proposes a mathematical model about the relationship between the curvature radius changes of interventricular septal configuration and the pressure difference of left and right ventricular based on Young-Laplace model. The method of noninvasive detection of pressure difference of left and right ventricular is discussed. In this paper a ventricular model which can simulate the deformation of interventricular septal configuration in differential pressure is designed and produced. After scanning the ventricular model by CT, the boundary of the ventricular model is extracted and the curvature radius of the boundary is calculated. Software ANSYS is used to simulate the curvature of the ventricular septum model and verify the experiment. Then a relationship between the deformation of ventricular septum and the pressure difference of ventricle was established by the Hooke's law combined with the modified Young-Laplace model. After the verification, the modified Young-Laplace model can be applied to estimate pressure difference in ventricle. This paper contributes the development of noninvasive medical diagnosis of abnormal ventricular pressure. © 2016 IEEE.

关键词： Diagnosis

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

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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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A local-optimization refinement algorithm in single particle analysis for macromolecular complex with multiple rigid modules

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Protein & Cell 2016年第1期7卷 46-62页

作者： Hong Shan Zihao wang Fa Zhang Yong Xiong Chang-Cheng Yin Fei Sun Department of Biophysics College of Basic Medical Sciences Peking University Health Science Center Beijing 100191China National Laboratory of Biomacromolecules Institute of Biophysics Chinese Academy of Sciences Beijing 100101 China Key Lab of Intelligent Information Processing and Advanced Computing Research Lab Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06511 USA Center for Biological Imaging Institute of Biophysics Chinese Academy of Sciences Beijing 100101 China

Single particle analysis, which can be regarded as an average of signals from thousands or even millions of particle projections, is an efficient method to study the three-dimensional structures of biological macro- molecules. An intrinsic assumption in single particle analysis is that all the analyzed particles must have identical composition and conformation. Thus specimen heterogeneity in either composition or conformation has raised great challenges for high-resolution analysis. For particles with multiple conformations, inaccurate align- ments and orientation parameters will yield an averaged map with diminished resolution and smeared density. Besides extensive classification approaches, here based on the assumption that the macromolecular complex is made up of multiple rigid modules whose relative orien- tations and positions are in slight fluctuation around equilibriums, we propose a new method called as local optimization refinement to address this conformational heterogeneity for an improved resolution. The key idea is to optimize the orientation and shift parameters of each rigid module and then reconstruct their three-dimen- sional structures individually. Using simulated data of 80S/70S ribosomes with relative fluctuations between the large （60S/50S） and the small （40S/30S） subunits, we tested this algorithm and found that the resolutions of both subunits are significantly improved. Our method provides a proof-of-principle solution for high-resolutionsingle particle analysis of macromolecular complexes with dynamic conformations.

关键词： cryo-electron microscopy single particleanalysis conformational heterogeneity rigid module Iocaoptimization refinement

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Consensus algorithms for biased labeling in crowdsourcing

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Information Sciences 2017年 382-383卷 254-273页

作者： Jing Zhang Victor S. Sheng Qianmu Li Jian Wu Xindong Wu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 P. R. China Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing 210094 P. R. China Department of Computer Science University of Central Arkansas Conway AR 72035 USA Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 P. R. China Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 P. R. China School of Computing and Informatics University of Louisiana at Lafayette LA 70504 USA

Although it has become an accepted lay view that when labeling objects through crowdsourcing systems, non-expert annotators often exhibit biases, this argument lacks sufficient evidential observation and systematic empirical study. This paper initially analyzes eight real-world datasets from different domains whose class labels were collected from crowdsourcing systems. Our analyses show that biased labeling is a systematic tendency for binary categorization; in other words, for a large number of annotators, their labeling qualities on the negative class (supposed to be the majority) are significantly greater than are those on the positive class (minority). Therefore, the paper empirically studies the performance of four existing EM-based consensus algorithms , DS, GLAD, RY, and ZenCrowd, on these datasets. Our investigation shows that all of these state-of-the-art algorithms ignore the potential bias characteristics of datasets and perform badly although they model the complexity of the systems. To address the issue of handling biased labeling, the paper further proposes a novel consensus algorithm, namely adaptive weighted majority voting (AWMV), based on the statistical difference between the labeling qualities of the two classes. AWMV utilizes the frequency of positive labels in the multiple noisy label set of each example to obtain a bias rate and then assigns weights derived from the bias rate to negative and positive labels. Comparison results among the five consensus algorithms (AWMV and the four existing) show that the proposed AWMV algorithm has the best overall performance. Finally, this paper notes some potential related topics for future study.

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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 New Fast Chaos-based image Cipher with a Novel Permuatation Approach

A New Fast Chaos-based Image Cipher with a Novel Permuatatio...

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2016 IEEE 13th International Conference on Signal Processing（ICSP2016）

作者： Chong Fu Qing-fu Wang Zhe Chen Shao-ting Chen Hui-yan Jiang School of Computer Science and Engineering Northeastern University Liaoning School of Administration Software College of Ministry of Education Northeastern University Key Laboratory of Medical Image Computing of Ministry of Education Northeastern University

This paper suggests a new fast chaos-based image cipher with a permutation-diffusion structure. In the permutation stage, the original image is scrambled by swapping each pixel with another one at a location chosen by a pseudorandom permutation generated from the logistic map. The initial condition of the map, i.e. the permutation key, is determined by the City Hash value of the original image. Owing to the avalanche property of a hash function and the initial condition sensitivity property of a chaotic system, pixels in two plain-images will be shuffled in different ways even if there is a tiny difference between them. Such a plaintext-dependent permutation method effectively accelerates the diffusion process and increases the diffusion intensity, and consequently the minimum number of diffusion(overall) rounds required to achieve a satisfactory level of security is reduced. In the diffusion stage, the shuffled image data are masked by a keystream extracted from the orbit of Lü system. Thorough security analysis is carried out, and the results demonstrate the satisfactory security of the proposed scheme.

关键词： image cipher plaintext-dependent permutation City Hash logistic map Lü system

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A multi-agent coordinated planning approach for deadline required emergency response tasks

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IET Control Theory and Applications 2015年第3期9卷 447-455页

作者： Zhou, Chao Wang, Hongwei Zhuo, Hankz Hankui Key Laboratory of Image Processing and Intelligent Control of Education Ministry Institute of Systems Engineering Huazhong University of Science and Technology Wuhan430074 China School of Advanced Computing SunYat-sen University Guangzhou510006 China

This study investigates the emergency decision-making problem in a multi-agent system. Departments are modeled as agents to perform coordinated planning to obtain a global action plan with a short execution time constrained by a prescribed deadline. A novel multi-agent planning approach is proposed to coordinate for the solution among agents in the system. The approach consists of two stages. For the first stage, a coordinated planning and scheduling method is designed to generate a candidate global action plan. In this plan, durative actions are assigned to proper time intervals with the consideration of temporal relationships among agents. For the second stage, a coordination mechanism based on local heuristics is proposed to reduce the global time cost of the candidate plan to satisfy the deadline. An experimental study of emergency evacuation problem is conducted to demonstrate the effectiveness and efficiency of the proposed approach. © 2015 The Institution of Engineering and Technology.

关键词： Multi agent systems

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Pancreatic malformations as seen in congenital syndromes — Developmental perspective with an alternative view

Pancreatic malformations as seen in congenital syndromes — ...

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International Conference on Information and Automation (ICIA)

作者： Jianhua Li Yueyang Teng Shouliang Qi Dayu Xiao Lisheng Xu Yan Kang Jesse Li-Ling Sino-Dutch Biomedical and Information Engineering School Northeastern University Shenyang China Ministry of Education Key Laboratory of Medical Image Computing Shenyang China Institute of Genetic Medicine Sichuan University Chengdu China

ISBN: (纸本)9781509041039

Symptomatic patterns of congenital syndromes may be indicative of common developmental mechanisms. A striking similarity has been discovered between the clinical features of multiple congenital anomaly/mental retardation syndromes (MCA/MRs) and the meridians and Zang-Fu theories of Traditional Chinese Medicine (TCM). In this study, an effort has been made to compare the features of MCA/MRs involving various pancreatic malformations with the TCM theories. Clinical synopses of pancreatic malformations were retrieved from the Online Mendelian Inheritance in Man (OMIM) database. Following the standardization of the phenotypic features, frequently co-occurring malformations among such MCA/MRs were discovered using a frequent-pattern mining algorithm. A strong correlation has been discovered between the features of the listed MCA/MRs and the TCM concepts. Pancreatic abnormalities seem to be a constituent part of the midline defect spectrum. Embryonic development of pancreas, liver and kidney are closely related. The pancreas, at least in part, has been incorporated into the TCM concept of the “Kidneys”. The strong correlation between the MCA/MRs and meridians suggests that the latter are genuine reflections of the connections between particular organs during embryonic development. Although the pancreas is a “missed” organ in the TCM theories, its conceptual incorporation into the kidneys is in fact very useful, which can provide new insights into the pathogenesis of pancreatic disorders and lead to more effective treatment for pancreas-related diseases.

关键词： Pancreas Kidney Liver Diabetes Heart Glands

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