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arXiv 2019年

作者： Zhou, Chenhong Ding, Changxing Wang, Xinchao Lu, Zhentai Tao, Dacheng School of Electronic and Information Engineering South China University of Technology Guangzhou510000 China Department of Computer Science Stevens Institute of Technology Hoboken United States Guangdong Provincial Key Laboratory of Medical Image Processing School of Biomedical Engineering Southern Medical University Guangzhou China UBTech Sydney Artificial Intelligence Centre School of Information Technologies Faculty of Engineering and Information Technologies University of Sydney DarlingtonNSW2008 Australia

Class imbalance has emerged as one of the major challenges for medical image segmentation. The model cascade (MC) strategy, a popular scheme, significantly alleviates the class imbalance issue via running a set of individual deep models for coarse-to-fine segmentation. Despite its outstanding performance, however, this method leads to undesired system complexity and also ignores the correlation among the models. To handle these flaws in the MC approach, we propose in this paper a light-weight deep model, i.e., the One-pass Multi-task Network (OM-Net) to solve class imbalance better than MC does, while requiring only one-pass computation for brain tumor segmentation. First, OM-Net integrates the separate segmentation tasks into one deep model, which consists of shared parameters to learn joint features, as well as task-specific parameters to learn discriminative features. Second, to more effectively optimize OM-Net, we take advantage of the correlation among tasks to design both an online training data transfer strategy and a curriculum learning-based training strategy. Third, we further propose sharing prediction results between tasks, which enables us to design a cross-task guided attention (CGA) module. By following the guidance of the prediction results provided by the previous task, CGA can adaptively recalibrate channel-wise feature responses based on the category-specific statistics. Finally, a simple yet effective post-processing method is introduced to refine the segmentation results of the proposed attention network. Extensive experiments are conducted to demonstrate the effectiveness of the proposed techniques. Most impressively, we achieve state-of-the-art performance on the BraTS 2015 testing set and BraTS 2017 online validation set. Using these proposed approaches, we also won joint third place in the BraTS 2018 challenge among 64 participating teams. The code is publicly available at https://***/chenhong-zhou/OM-Net. Copyright © 2019, The Author

关键词： Magnetic resonance imaging

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Image reconstruction with low-rankness and self-consistency of k-space data in parallel MRI

arXiv

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arXiv 2019年

作者： Zhang, Xinlin Guo, Di Huang, Yiman Chen, Ying Wang, Liansheng Huang, Feng Qu, Xiaobo Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen361005 China School of Computer and Information Engineering Fujian Provincial University Key Laboratory of Internet of Things Application Technology Xiamen University of Technology Xiamen361024 China Department of Computer Science School of Information Science and Engineering Xiamen University Xiamen361005 China Neusoft Medical System Shanghai200241 China

Parallel magnetic resonance imaging has served as an effective and widely adopted technique for accelerating scans. The advent of sparse sampling offers aggressive acceleration, allowing flexible sampling and better reconstruction. Nevertheless, faithfully reconstructing the image from limited data still poses a challenging task. Recent low-rank reconstruction methods exhibit superiority in providing a high-quality image. However, none of them employ the routinely acquired calibration data for improving image quality in parallel magnetic resonance imaging. In this work, an image reconstruction approach named STDLR-SPIRiT was proposed to explore the simultaneous two-directional low-rankness (STDLR) in the k-space data and to mine the data correlation from multiple receiver coils with the iterative self-consistent parallel imaging reconstruction (SPIRiT). The reconstruction problem was then solved with a singular value decomposition-free numerical algorithm. Experimental results of phantom and brain imaging data show that the proposed method outperforms the state-of-the-art methods in terms of suppressing artifacts and achieving the lowest error. Moreover, the proposed method exhibits robust reconstruction even when the auto-calibration signals are limited in parallel imaging. Overall the proposed method can be exploited to achieve better image quality for accelerated parallel magnetic resonance imaging. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Retraction notice to "Predictive modeling the probability of suffering from metabolic syndrome using machine learning: A population-based study" [Heliyon 8 (2022) e12343]

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Heliyon 2025年第9期11卷

作者： Xiang Hu Xue-Ke Li Shiping Wen Xingyu Li Tian-Shu Zeng Jiao-Yue Zhang Weiqing Wang Yufang Bi Qiao Zhang Sheng-Hua Tian Jie Min Ying Wang Geng Liu Hantao Huang Miaomiao Peng Jun Zhang Chaodong Wu Yu-Ming Li Hui Sun Guang Ning Lu-Lu Chen Department of Endocrinology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders Wuhan China Centre for Artificial Intelligence Faculty of Engineering Information Technology University of Technology Sydney Ultimo NSW 2007 Australia School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Endocrinology and Metabolism State Key Laboratory of Medical Genomes National Clinical Research Center for Metabolic Diseases Shanghai Clinical Center for Endocrine and Metabolic Diseases Shanghai Institute of Endocrine and Metabolic Diseases Ruijin Hospital Shanghai Jiao-Tong University School of Medicine Shanghai China Department of Cardiovascular Surgery Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Yiling Hospital Yichang China Department of Nutrition and Food Science Texas A&M University College Station TX USA

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Gas Composition Recognition Based on Analyzing Acoustic Relaxation Absorption Spectra:Wavelet Decomposition and Support Vector Machine Classifier

Gas Composition Recognition Based on Analyzing Acoustic Rela...

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2018 2nd International Conference on Electrical Engineering and Automation（ICEEA2018）

作者： Yaqiong Jia Bin Yu Mingdi Du Xiaoli Wang Department of Electronics and Information Engineering Key Laboratory of Signal &Information Processing Hunan Institute of Technology Department of Computer and Information Science Key Laboratory of Signal & Information Processing Hunan Institute of Technology

Gas acoustic spectrum represents properties of acoustic propagation, which can distinguish gas compositions. However in few existing methods of gas-composition-unknown recognition, the approaches of programmatically processing the acoustic spectrum curves have not yet been presented. We propose a method for gas-composition-unknown recognition by analyzing gas acoustic relaxation absorption spectrum(GARAS) based on wavelet multi-resolution analysis(MRA) and multi-class support vector machine(SVM). Features of GARAS are extracted by wavelet MRA, and then selected to obtain a few feature coefficients which are utilized to train and test multi-class SVM. Simulation results show that the proposed method completely classifies four examples of gas mixtures, and that it recognizes the mixtures with the same and similar concentration or temperature. This method realizes the numerically extracting and programmatically processing the information of GARAS, and implements gas-composition-unknown sensing based on acoustic spectrums.

关键词： gas compositions recognition gas acoustic relaxation absorption spectrum wavelet multi-resolution analysis multi-class support vector machine

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Medical Image Registration Based on Moving Manifold Regularization

Medical Image Registration Based on Moving Manifold Regulari...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Wei Zhang Huabing Zhou Yicheng Yang Changcai Yang Zhenghong Yu Hubei Provincial Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China College of Computer and Information Sciences Fujian Agriculture and Forestry University FuZhou China College of Mechanical and Electrical Engineering Guangdong Polytechnic of Science and Technology Zhuhai China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

We propose a new registration framework, namely moving manifold regularization, for solving medical image registration problems. The proposed method first uses the Snake model to obtain the region of interest on the images, and then extracts the two landmark point sets from the segmented images. After that, a transformation model based on the two landmark point sets is used to register the medical images. This framework considers the error functional with moving manifold regularization as we formulate this transformation model as a vector-field interpolation problem. The regularized error function moves by pixels, and the manifold regularization is used as the prior of the transformation to capture the underlying geometry information of the input data. The transformation function for each pixel is specified in a special space namely Reproducing Kernel Hilbert Space. Extensive experiments on medical images showed that the proposed method, which overcomes the defects of manual selection error and improves the registration accuracy, is an effective and stable registration method.

关键词： Manifolds Medical diagnostic imaging Feature extraction Image registration Geometry Interpolation

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An Efficient Genetic Algorithm for Solving Constraint Shortest Path Problem Through Specified Vertices 1

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13th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2018

作者： Kai, Zhang Yunfeng, Shao Zhaozong, Zhang Wei, Hu School of Computer Science Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430081 China

ISBN: (数字)9789811328268

ISBN: (纸本)9789811328251

Finding a constraint shortest path which passes through a set of specified vertices is very important for many research areas, such as intelligent transportation systems, emergency rescue, and military planning. In this paper, we propose an efficient genetic algorithm for solving the constraint shortest path problem. Firstly, the Dijkstra algorithm is used to calculate the shortest distance between any two specified vertices. The optimal solution change from the original problem into the Hamilton path problem with the specified vertices. Because the number of specified vertices is much less than the number of vertices for the whole road network, the search space would be reduced exponentially. Secondly, the genetic algorithm is adopted to search for the optimal solution of the Hamilton path problem. Thirdly our algorithm should detect and eliminate the cycle path. Finally, the performance of our algorithm is evaluated by some real-life city road networks and some randomly generated road networks. The computational results show that our algorithm can find the constraint shortest path efficiently and effectively. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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Security analysis of practical continuous-variable quantum key distribution systems under laser seeding attack

arXiv

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arXiv 2019年

作者： Zheng, Yi Huang, Peng Huang, Anqi Peng, Jinye Zeng, Guihua College of Information Science and Technology Northwest University Xi’anShaanxi710127 China State Key Laboratory of Advanced Optical Communication Systems and Networks Center of Quantum Information Sensing and Processing Shanghai Jiao Tong University Shanghai200240 China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Greatwall Quantum Laboratory China Greatwall Technology Changsha410205 China

Here, we investigate the security of the practical one-way CVQKD and CV-MDI-QKD systems under laser seeding attack. In particular, Eve can inject a suitable light into the laser diodes of the light source modules in the two kinds of practical CVQKD systems, which results in the increased intensity of the generated optical signal. The parameter estimation under the attack shows that the secret key rates of these two schemes may be overestimated, which opens a security loophole for Eve to successfully perform an intercept-resend attack on these systems. To close this loophole, we propose a real-time monitoring scheme to precisely evaluate the secret key rates of these schemes. The analysis results indicate the implementation of the proposed monitoring scheme can effectively resist this potential attack. Copyright © 2019, The Authors. All rights reserved.

关键词： Light sources

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A convergence proof of projected fast iterative soft-thresholding algorithm for parallel magnetic resonance imaging

arXiv

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arXiv 2019年

作者： Zhang, Xinlin Lu, Hengfa Guo, Di Bao, Lijun Huang, Feng Qu, Xiaobo Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen361005 China School of Computer and Information Engineering Fujian Provincial University Key Laboratory of Internet of Things Application Technology Xiamen University of Technology Xiamen361024 China Neusoft Medical System Shanghai200241 China

The boom of non-uniform sampling and compressed sensing techniques dramatically alleviates the prolonged data acquisition problem of magnetic resonance imaging. Sparse reconstruction, thanks to its fast computation and promising performance, has attracted researchers to put numerous efforts on it and has been adopted in commercial scanners. Algorithms for solving the sparse reconstruction models play an essential role in sparse reconstruction. Being a simple and efficient algorithm for sparse reconstruction, pFISTA has been successfully extended to parallel imaging, however, its convergence criterion is still an open question, confusing users on the setting of the parameter which assures the convergence of the algorithm. In this work, we prove the convergence of the parallel imaging version pFISTA. Specifically, the convergences of two well-known parallel imaging reconstruction models, SENSE and SPIRiT, solved by pFISTA are proved. Experiments on brain images demonstrate the validity of the convergence criterion. The convergence criterion proofed in this work can help users quickly obtain the satisfy parameter that admits faithful results and fast convergence speeds. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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A compact core-jet structure in the changing-look seyfert NGC 2617

arXiv

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arXiv 2021年

作者： Yang, Jun Paragi, Zsolt Beswick, Robert J. Chen, Wen van Bemmel, Ilse M. Wu, Qingwen An, Tao Wu, Xiaocong Fan, Lulu Oonk, J.B.R. Liu, Xiang Wang, Weihua Department of Space Earth and Environment Chalmers University of Technology Onsala Space Observatory OnsalaSE-439 92 Sweden Postbus 2 DwingelooAANL-7990 Netherlands Jodrell Bank Centre for Astrophysics School of Physics and Astronomy The University of Manchester ManchesterM13 9PL United Kingdom Yunnan Observatories Chinese Academy of Sciences 396 Yangfangwang Guandu District Kunming650216 China Department of Astronomy School of Physics Huazhong University of Science and Technology Wuhan430074 China Shanghai Astronomical Observatory Key Laboratory of Radio Astronomy Chinese Academy of Sciences Shanghai200030 China CAS Key Laboratory for Research in Galaxies and Cosmology Department of Astronomy University of Science and Technology of China Hefei230026 China School of Astronomy and Space Sciences University of Science and Technology of China Hefei230026 China Shandong Provincial Key Lab of Optical Astronomy and Solar-Terrestrial Environment Institute of Space Science Shandong University Weihai264209 China SURFsara PO Box 94613 AmsterdamGPNL-1090 Netherlands Leiden Observatory Leiden University PO Box 9513 LeidenRANL-2300 Netherlands DwingelooPDNL-7991 Netherlands Xinjiang Astronomical Observatory Key Laboratory of Radio Astronomy Chinese Academy of Sciences 150 Science 1-Street Urumqi830011 China School of Computer Information Engineering Changzhou Institute of Technology Changzhou213032 China

The nearby face-on spiral galaxy NGC 2617 underwent an unambiguous ‘inside-out’ multi-wavelength outburst in Spring 2013, and a dramatic Seyfert type change probably between 2010 and 2012, with the emergence of broad optical emission lines. To search for the jet activity associated with this variable accretion activity, we carried out multi-resolution and multi-wavelength radio observations. Using the very long baseline interferometric (VLBI) observations with the European VLBI Network (EVN) at 1.7 and 5.0 GHz, we find that NGC 2617 shows a partially synchrotron self-absorbed compact radio core with a significant core shift, and an optically thin steep-spectrum jet extending towards the north up to about two parsecs in projection. We also observed NGC 2617 with the electronic Multi-Element Remotely Linked Interferometer Network (e-MERLIN) at 1.5 and 5.5 GHz, and revisited the archival data of the Very Large Array (VLA) and the Very Long Baseline Array (VLBA). The radio core had a stable flux density of ∼1.4 mJy at 5.0 GHz between 2013 June and 2014 January, in agreement with the expectation of a supermassive black hole in the low accretion rate state. The northern jet component is unlikely to be associated with the ‘inside-out’ outburst of 2013. Moreover, we report that most optically selected changing-look AGN at © 2021, CC BY-NC-ND.

关键词： Radio astronomy

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Practical security of continuous-variable quantum key distribution with reduced optical attenuation

arXiv

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arXiv 2019年

In a practical CVQKD system, the optical attenuator can adjust the Gaussian-modulated coherent states and the local oscillator signal to an optimal value for guaranteeing the security of the system and optimizing the performance of the system. However, the performance of the optical attenuator may deteriorate due to the intentional and unintentional damage of the device. In this paper, we investigate the practical security of a CVQKD system with reduced optical attenuation. We find that the secret key rate of the system may be overestimated based on the investigation of parameter estimation under the effects of reduced optical attenuation. This opens a security loophole for Eve to successfully perform an intercept-resend attack in a practical CVQKD system. To close this loophole, we add an optical fuse at Alice’s output port and design a scheme to monitor the level of optical attenuation in real time, which can make the secret key rate of the system evaluated precisely. The analysis shows that these countermeasures can effectively resist this potential attack. Copyright © 2019, The Authors. All rights reserved.

关键词： Electromagnetic wave attenuation

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