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检索条件"机构=Key Laboratory of Intelligent Computing in Medical Image"

共 842 条记录，以下是341-350 订阅

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Research on Bi-node Initiation Restoration Method in Grooming Networks

Research on Bi-node Initiation Restoration Method in Groomin...

引用

2014 International Conference on Advanced Computer Science and Engineering(ACSE 2014)

作者： Yan-jie XUAN Shui-ling BAO Li DU Key Laboratory of Medical Image Computing of Ministry of Education Northeastern University College of Information Science and Engineering Northeastern University

This paper proposes a bi-node initiation restoration method(BNRM) against the Survivability problems of grooming networks. BNRM allows both source node and destination node of a broken connection to respectively initiate a restoration process in the event of a link failure. A complete dynamic restoration method is composed of restoration protocol and rerouting algorithm. This paper proposes the bi-node initiation restoration protocol(BNRP), and studies the dynamic grooming algorithm based on the link bundled auxiliary graph(DGAL) as the rerouting algorithm. Simulation shows that BNRM reduces the failure restoration time compared with other restoration methods without increasing request blocking rate. And traffic grooming technology can improve the failure restoration rate as well as reduce failure restoration time.

关键词： Traffic grooming Bi-node initiation restoration protocol Bi-node initiation restoration method

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Breast Tumor Detection in Double Views Mammography Based on Simple Bias

Breast Tumor Detection in Double Views Mammography Based on ...

引用

2013 IEEE International Conference on medical Imaging Physics and Engineering

作者： Shiya Zhang Zhongzhou Chen Sheng Gu Xihe Qiu Qixun Qu Zhiqiong Wang Sino-Dutch Biomedical & Information Engineering School Northeastern University Key Laboratory of Medical Image Computing Ministry of Education

Breast tumor detection is a most effective way to immunized against mammary *** is known that the sort algorithm of extreme learning machine(ELM),in view of the feature model for breast X-ray image,is being applied in the computer aided detection of breast *** the basis of all these,it is raised in this paper that marking for the suspicious region in the double view mammography by the use of ELM,then classifying the result of double views marking by using the Simple Bias classifier and finally gaining the detection *** experiment with 444 cases or 222 pair of X-ray mammography from Liao Ning Province Cancer Hospital shows that,the breast tumor detection in double views mammography based on Simple Bias is an available and effective way to detect breast tumor.

关键词： Extreme learning machine,Simple Bias,mammography,double views,tumor detection

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iHDFS:A Distributed File System Supporting Incremental computing

iHDFS:A Distributed File System Supporting Incremental Compu...

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International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015

作者： Zhenhua Wang Qingsong Ding Fuxiang Gao Derong Shen Ge Yu College of Information Science and Engineering Northeastern University Key Laboratory of Medical Image Computing Northeastern UniversityMinistry of Education

Big data are always processed repeatedly with small changes,which is a major form of big data *** feature of incremental change of big data shows that incremental computing mode can improve the performance *** is a distributed file system on Hadoop which is the most popular platform for big data *** HDFS adopts fixed-size chunking policy,which is inefficient facing incremental ***,in this paper,we proposed i HDFS(incremental HDFS),a distributed file system,which can provide basic guarantee for big data parallel *** i HDFS is implemented as an extension to *** i HDFS,Rabin fingerprint algorithm is applied to achieve content defined *** policy make data chunking has much higher stability,and the intermediate processing results can be reused efficiently,so the performance of incremental data processing can be improved *** effectiveness and efficiency of i HDFS have been demonstrated by the experimental results.

关键词： incremental computing distributed file system big data HDFS

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Cost sensitive adaptive random subspace ensemble for computer-aided nodule detection

Cost sensitive adaptive random subspace ensemble for compute...

引用

IEEE International Symposium on Computer-Based medical Systems

作者： Peng Cao Dazhe Zhao Osmar Zaiane Key Laboratory of Medical Image Computing of Ministry of Education Northeastern University China University of Alberta Edmonton Alberta Canada

ISBN: (纸本)9781479910526

Many lung nodule computer-aided detection methods have been proposed to help radiologists in their decision making. Because high sensitivity is essential in the candidate identification stage, there are countless false positives produced by the initial suspect nodule generation process, giving more work to radiologists. The difficulty of false positive reduction lies in the variation of the appearances of the potential nodules, and the imbalance distribution between the amount of nodule and non-nodule candidates in the dataset. To solve these challenges, we extend the random subspace method to a novel Cost Sensitive Adaptive Random Subspace ensemble (CSARS), so as to increase the diversity among the components and overcome imbalanced data classification. Experimental results show the effectiveness of the proposed method in terms of G-mean and AUC in comparison with commonly used methods.

关键词： Cost random ensemble

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Modeling and stability analysis of the propagation of a worm

Modeling and stability analysis of the propagation of a worm

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2013 International Conference on Sport Science and Computer Science (CCCS 2013)

作者： Yu Yao Zhao Zhang Shuo Li Fuxiang Gao Ge Yu College of Information Science and Engineering Northeastern University Key Laboratory of Medical Image Computing Northeastern UniversityMinistry of Education

Internet worm can spread not only via networks but also through external removable *** this paper,considering the influence of removable media,we construct two new worm propagation *** analyzing the local asymptotic stability at infection-free equilibrium,a basic reproduction number is *** is locally stable at infection-free equilibrium if basic reproduction number is less than one;whereas system will lose its *** numerical experiments are given to support the theoretical ***,simulating the propagation behavior via simulation experiments,we further demonstrate the correctness of theoretical analysis.

关键词： worm propagation models removable media infection-free equilibrium basic reproduction number simulation

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Motion Classification Based on sEMG Signals Using Deep Learning 4th

Motion Classification Based on sEMG Signals Using Deep Learn...

引用

4th International Conference on Machine Learning and intelligent Communications, MLICOM 2019

作者： Shen, Shu Gu, Kang Chen, Xinrong Wang, Ruchuan School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing210023 China Academy for Engineering and Technology Fudan University Shanghai200433 China Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Shanghai200032 China

ISBN: (纸本)9783030323875

Nowadays, surface electromyography (sEMG) signal plays an important role in helping physically disabled people during daily life. The development of electronic information technology has also led to the emergence of low-cost, multi-channel, wearable sEMG signal acquisition devices. Therefore, this paper proposes a new motion recognition model based on deep learning to improve the accuracy of motion recognition of sEMG signals. The model uses architecture including 6 convolutional layers and 6 pooling layers which enables the Batch Normalization layer to enhance network performance and prevent overfitting. In the experiment, NinaPro DB5 data set was used for training and testing. The data set consists of sEMG signal data collected by the double Myo armbands which contains data of 52 movements for 10 subjects. The results show that the accuracy of about 90% can be achieved when using 52 sEMG signal data from every single subject or all subjects. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Deep learning

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Assisted System for Precise Surgery Based on medical image computing and VR

Assisted System for Precise Surgery Based on Medical Image C...

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第30届中国控制与决策会议

作者： Zhang Mingshi Sha Hengyu Ren Zebin Tan Wenjun School of Computer Science and Engineering Northeastern University Key Laboratory of Medical Image Computing of Ministry of Education Northeastern University

Virtual assisted surgery system in the preoperative planning, teaching, communication between doctors and patients has an important application value. Due to the hardware and computer technology constraints, most of the research on the virtual surgery system based on 3 D software model simulation to simulate human tissue. To solve the problem, an assisted system for precise surgery based on medical image computing and VR is designed in this paper. Firstly, this work designed the overall scheme of the system, including function design, hardware design scheme, software design scheme and key technology, starting from the system requirements and feasibility. Secondly, the work studies the key technologies of virtual assistant surgery system, such as the generation of human tissue model and the collision detection. In order to improve the quality of grid generation and remove noise, a tetrahedral mesh generation method based on grid and improved Laplacian smoothing algorithm is adopted in this work. Finally, the modular idea is used, respectively, to start UI and virtual surgery scene construction and interaction,independent units and the overall integration test to ensure that the system is running without errors and fluent, to achieve the original design of virtual assistant surgery system.

关键词： Virtual assisted surgery system Tetrahedral mesh Grid collision detection Bounding box

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Diurnal Forced Models for Worm Propagation Based on Conficker Dataset

Diurnal Forced Models for Worm Propagation Based on Conficke...

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International Conference on Multimedia Information Networking and Security (MINES)

作者： Yu Yao Wen-long Xiang Hao Guo Ge Yu Fu-xiang Gao Key Laboratory of Medical Image Computing Ministry of Education School of Information Science and Engineering Northeastern University Shenyang China

Due to the vulnerability of computer system, Internet worm is still greatly threatenning network security. It is found that the spread of worm regularly change because of the different space distributions of hosts and this phenomenon is defined as diurnal pattern. Based on diurnal pattern, two worm propagation models, namely simple diurnal forced model and complicated diurnal forced one, are constructed to describe and predict worm propagation. To verify the accuracy of them, Conficker dataset is adopted to obtain the practical curves of worm propagation. Through comparisons of numerical curves from models and practical curves, it is proved that simple model is good in describing the initial stage of worm propagation and complicated model can accurately predict the whole worm propagation.

关键词： Grippers Numerical models Computational modeling Security Predictive models Mathematical model Data models

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Automatic Detection of ECG Abnormalities by Using an Ensemble of Deep Residual Networks with Attention 1st

Automatic Detection of ECG Abnormalities by Using an Ensembl...

引用

1st International Workshop on Machine Learning and medical Engineering for Cardiovasvular Healthcare, MLMECH 2019, and the 8th International Joint Workshops on computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2019, held in conjunction with 22nd International Conference on medical image computing and Computer-Assisted Intervention, MICCAI 2019

作者： Liu, Yang He, Runnan Wang, Kuanquan Li, Qince Sun, Qiang Zhao, Na Zhang, Henggui Harbin150001 China School of Physics and Astronomy The University of Manchester ManchesterM13 9PL United Kingdom SPACEnter Space Science and Technology Institute Shenzhen518117 China International Laboratory for Smart Systems and Key Laboratory of Intelligent of Computing in Medical Image Ministry of Education Northeastern University Shenyang110004 China The Department of Pharmacology Beijing Electric Power Hospital Beijing100073 China

ISBN: (纸本)9783030333263

Heart disease is one of the most common diseases causing morbidity and mortality. Electrocardiogram (ECG) has been widely used for diagnosing heart diseases for its simplicity and non-invasive property. Automatic ECG analyzing technologies are expected to reduce human working load and increase diagnostic efficacy. However, there are still some challenges to be addressed for achieving this goal. In this study, we develop an algorithm to identify multiple abnormalities from 12-lead ECG recordings. In the algorithm pipeline, several preprocessing methods are firstly applied on the ECG data for denoising, augmentation and balancing recording numbers of variant classes. In consideration of efficiency and consistency of data length, the recordings are padded or truncated into a medium length, where the padding/truncating time windows are selected randomly to suppress overfitting. Then, the ECGs are used to train deep neural network (DNN) models with a novel structure that combines a deep residual network with an attention mechanism. Finally, an ensemble model is built based on these trained models to make predictions on the test data set. Our method is evaluated based on the test set of the First China ECG intelligent Competition dataset by using the F1 metric that is regarded as the harmonic mean between the precision and recall. The resultant overall F1 score of the algorithm is 0.875, showing a promising performance and potential for practical use. © 2019, Springer Nature Switzerland AG.

关键词： Electrocardiograms

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Heartbeat Detection and Rate Estimation from Ballistocardiograms using the Gated Recurrent Unit Network 42

Heartbeat Detection and Rate Estimation from Ballistocardiog...

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42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020

作者： Hai, Dong Chen, Chao Yi, Ruhan Gou, Shuiping Yu Su, Bo Jiao, Changzhe Skubic, Marjorie Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Ibm Research University of Missouri Eecs Dept. United States Brigham and Women Hospital Division of Sleep and Circadian Disorders Harvard Medical School United States

ISBN: (纸本)9781728119908

Inspired by the application of recurrent neural networks (RNNs) to image recognition, in this paper, we propose a heartbeat detection framework based on the Gated Recurrent Unit (GRU) network. In this contribution, the heartbeat detection task from ballistocardiogram (BCG) signals was modeled as a classification problem where the segments of BCG signals were formulated as images fed into the GRU network for feature extraction. The proposed framework has advantages in fusion of multi-channel BCG signals and effective extraction of the temporal and waveform characteristics of the heartbeat signal, thereby enhancing heart rate estimation accuracy. In laboratory collected BCG data, the proposed method achieved the best heart rate estimation results compared to previous algorithms. © 2020 IEEE.

关键词： Recurrent neural networks

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