检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Tian, Chunwei Zhang, Yanning Zuo, Wangmeng Lin, Chia-Wen Zhang, David Yuan, Yixuan The School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China The School of Computer Science Northwestern Polytechnical University The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China The School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China The Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan Guangdong Shenzhen518172 China The Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong

Convolutional neural networks (CNNs) have obtained remarkable performance via deep architectures. However, these CNNs often achieve poor robustness for image superresolution (SR) under complex scenes. In this paper, we present a heterogeneous group SR CNN (HGSRCNN) via leveraging structure information of different types to obtain a high-quality image. Specifically, each heterogeneous group block (HGB) of HGSRCNN uses a heterogeneous architecture containing a symmetric group convolutional block and a complementary convolutional block in a parallel way to enhance internal and external relations of different channels for facilitating richer low-frequency structure information of different types. To prevent appearance of obtained redundant features, a refinement block with signal enhancements in a serial way is designed to filter useless information. To prevent loss of original information, a multi-level enhancement mechanism guides a CNN to achieve a symmetric architecture for promoting expressive ability of HGSRCNN. Besides, a parallel up-sampling mechanism is developed to train a blind SR model. Extensive experiments illustrate that the proposed HGSRCNN has obtained excellent SR performance in terms of both quantitative and qualitative analysis. Codes can be accessed at https://***/hellloxiaotian/HGSRCNN. © 2022, CC BY-NC-SA.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Image super-resolution with an enhanced group convolutional neural network

arXiv

引用

arXiv 2022年

作者： Tian, Chunwei Yuan, Yixuan Zhang, Shichao Lin, Chia-Wen Zuo, Wangmeng Zhang, David School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China Department of Electrical Engineering City University of Hong Kong Hong Kong School of Computer Science and Engineering Central South University Hunan Changsha410083 China Department of Electrical Engineering the Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China Peng Cheng Laboratory Guangdong Shenzhen518055 China Guangdong Shenzhen518172 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. However, CNNs depend on deeper network architectures to improve performance of image super-resolution, which may increase computational cost in general. In this paper, we present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture by fully fusing deep and wide channel features to extract more accurate low-frequency information in terms of correlations of different channels in single image super-resolution (SISR). Also, a signal enhancement operation in the ESRGCNN is useful to inherit more long-distance contextual information for resolving long-term dependency. An adaptive up-sampling operation is gathered into a CNN to obtain an image super-resolution model with low-resolution images of different sizes. Extensive experiments report that our ESRGCNN surpasses the state-of-the-arts in terms of SISR performance, complexity, execution speed, image quality evaluation and visual effect in SISR. Code is found at https://***/hellloxiaotian/ESRGCNN. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

A systematic collection of medical image datasets for deep learning

arXiv

引用

arXiv 2021年

作者： Li, Johann Zhu, Guangming Hua, Cong Feng, Mingtao Bennamoun, Basheer Li, Ping Lu, Xiaoyuan Song, Juan Shen, Peiyi Xu, Xu Mei, Lin Zhang, Liang Shah, Syed Afaq Ali Bennamoun, Mohammed School of Computer Science and Technology Xidian University China School of Medicine University of Notre Dame Australia Data and Virtual Research Room Shanghai Broadband Network Center China Third Research Institute of The Ministry of Public Security China Discipline of Information Technology Media and Communications Murdoch University Australia Department of Computer Science and Software Engineering University of Western Australia Australia

The astounding success made by artificial intelligence (AI) in healthcare and other fields proves that AI can achieve human-like performance. However, success always comes with challenges. Deep learning algorithms are data-dependent and require large datasets for training. The lack of data in the medical imaging field creates a bottleneck for the application of deep learning to medical image analysis. Medical image acquisition, annotation, and analysis are costly, and their usage is constrained by ethical restrictions. They also require many resources, such as human expertise and funding. That makes it difficult for non-medical researchers to have access to useful and large medical data. Thus, as comprehensive as possible, this paper provides a collection of medical image datasets with their associated challenges for deep learning research. We have collected information of around three hundred datasets and challenges mainly reported between 2013 and 2020 and categorized them into four categories: head & neck, chest & abdomen, pathology & blood, and "others". Our paper has three purposes: 1) to provide a most up to date and complete list that can be used as a universal reference to easily find the datasets for clinical image analysis, 2) to guide researchers on the methodology to test and evaluate their methods’ performance and robustness on relevant datasets, 3) to provide a "route" to relevant algorithms for the relevant medical topics, and challenge leaderboards. © 2021, CC BY-NC-SA.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Weighted Linear Multiple Kernel Learning for Saliency Detection 2nd

Weighted Linear Multiple Kernel Learning for Saliency Detect...

引用

2nd EAI International Conference on Robotic Sensor Networks, ROSENET 2018

作者： Zhou, Quan Wu, Jinwen Fan, Yawen Zhang, Suofei Wu, Xiaofu Zheng, Baoyu Jin, Xin Lu, Huimin Latecki, Longin Jan National Engineering Research Center of Communications and Networking Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China College of Information Engineering China University of Geosciences Wuhan China School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China National Engineering Research Center of Communications and Networking Nanjing University of Posts and Telecommunications Nanjing China Department of Cyber Security Beijing Electronic Science and Technology Institute Beijing China CETC Big Data Research Institute Co. Ltd. Guizhou Guiyang China Department of Mechanical and Control Engineering Kyushu Institute of Technology Kitakyushu Japan Department of Computer and Information Sciences Temple University PhiladelphiaPA United States

ISBN: (纸本)9783030177621

This paper presents a novel saliency detection method based on weighted linear multiple kernel learning (WLMKL), which is able to adaptively combine different contrast measurements in a supervised manner. Three commonly used bottom-up visual saliency operations are first introduced, including corner-surround contrast (CSC), center-surround contrast (CESC), and global contrast (GC). Then these contrast measures are fed into our WLMKL framework to produce the final saliency map. We show that the assigned weights for each contrast feature maps are always normalized in our WLMKL formulation. In addition, the proposed approach benefits from the advantages of the contribution of each individual contrast operation, and thus produces more robust and accurate saliency maps. The extensive experimental results show the effectiveness of the proposed model, and demonstrate the combination is superior to individual subcomponent. © 2020, Springer Science and Business Media Deutschland GmbH. All rights reserved.

关键词： Center-surround contrast Corner-surround contrast Global contrast Multiple kernel learning Saliency detection

来源：评论

学校读者我要写书评

暂无评论

Multi-operator network sharing for massive IoT

arXiv

引用

arXiv 2020年

作者： Xiao, Yong Krunz, Marwan Shu, Tao School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering the University of Arizona TucsonAZ85721 United States School of Electrical and Data Engineering University of Technology Sydney UltimoNSW2007 Australia Department of Computer Science and Software Engineering Auburn University AuburnAL36849 United States

A recent study predicts that by 2020, up to 50 billion Internet-of-Things (IoT) devices will be connected to the Internet, straining the capacity of the wireless infrastructure, which has already been overloaded with data-hungry mobile applications. How to accommodate the demand for both massivescale IoT devices and high-speed cellular services in the physically limited spectrum without significantly increasing the operational and infrastructure costs is one of the main challenges for operators. In this article, we introduce a new multi-operator network sharing framework that supports the coexistence of IoT and high-speed cellular services. Our framework is based on the radio access network (RAN) sharing architecture recently introduced by 3GPP as a promising solution for operators to improve their resource utilization and reduce system roll-out cost. We evaluate the performance of our proposed framework using real base station location data in the city of Dublin collected from two major operators in Ireland. Numerical results show that our proposed framework can almost double the total number of supported IoT devices and simultaneously coexist with other cellular services. Copyright © 2020, The Authors. All rights reserved.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions

arXiv

引用

arXiv 2022年

作者： Nan, Yang Del Ser, Javier Walsh, Simon Schönlieb, Carola Roberts, Michael Selby, Ian Howard, Kit Owen, John Neville, Jon Guiot, Julien Ernst, Benoit Pastor, Ana Alberich-Bayarri, Angel Menzel, Marion I. Walsh, Sean Vos, Wim Flerin, Nina Charbonnier, Jean-Paul van Rikxoort, Eva Chatterjee, Avishek Woodruff, Henry Lambin, Philippe Cerdá-Alberich, Leonor Martí-Bonmatí, Luis Herrera, Francisco Yang, Guang Xing, Xiaodan Li, Ming Wagers, Scott Baker, Rebecca Nardi, Cosimo van Eeckhout, Brice Skipp, Paul Powell, Pippa Carroll, Miles Ruggiero, Alessandro Thillai, Muhunthan Babar, Judith Sala, Evis Murch, William Hiscox, Julian Baralle, Diana Sverzellati, Nicola Blanco, Ana Miguel Bataller, Fuensanta Bellvís Aznar, Mario Suarez, Amelia Figueiras, Sergio Krischak, Katharina Hierath, Monika Mirsky, Yisroel Elovici, Yuval Beregi, Jean Paul Fournier, Laure Sardanelli, Francesco Penzkofer, Tobias Seymour, Karine Blanquer, Nacho Neri, Emanuele Laghi, Andrea França, Manuela Martinez, Ricard National Heart and Lung Institute Imperial College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital London United Kingdom School of Biomedical Engineering & Imaging Sciences King's College London London United Kingdom Department of Communications Engineering University of the Basque Country UPV/EHU Bilbao48013 Spain Derio48160 Spain Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Oncology R&D AstraZeneca Cambridge United Kingdom Department of Radiology University of Cambridge Cambridge United Kingdom Clinical Data Interchange Standards Consortium AustinTX United States Respiratory medicine department Liège Belgium University of Liege Department of Clinical Sciences Pneumology-Allergology Liège Belgium QUIBIM Valencia Spain Technische Hochschule Ingolstadt Ingolstadt Germany GE Healthcare GmbH Munich Germany Liège Belgium Thirona Nijmegen Netherlands Department of Precision Medicine Maastricht University Maastricht Netherlands Medical Imaging Department Hospital Universitari i Politècnic La Fe Valencia Spain University of Granada Granada Spain Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia National Heart and Lung Institute Imperial College London London United Kingdom BioSci Consulting Maasmechelen Belgium Clinical Data Interchange Standards Consortium AustinTX United States University of Florence Firenze Italy Medical Cloud Company Liège Belgium TopMD Southampton United Kingdom European Lung Foundation Sheffield United Kingdom Department of Health Public Health England London United Kingdom Department of Radiology University of Cambridge Cambridge United Kingdom Owlstone Medical Cambridge United Kingdom University of Liverpool Liverpool United Kingdom University of Southampton Southampton United Kingdom University of Parma Parma Italy Medical Imaging Department Hospital Universi

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research. © 2022, CC BY-NC-ND.

关键词： Health care

来源：评论

学校读者我要写书评

暂无评论

Brain tumor segmentation from multispectral MRIs using sparse representation classification and markov random field regularization

International Journal of Signal Processing, Image Processing...

引用

International Journal of Signal Processing, Image Processing and Pattern Recognition 2015年第9期8卷 229-238页

作者： Zhan, Tianming Gu, Shenghua Feng, Can Zhan, Yongzhao Wang, Jin School of Computer Science & communications Engineering Jiangsu University Zhenjiang China Jiangsu Key Laboratory of Big Data Analysis Technology Nanjing University of Information Science and Technology Nanjing China Center of software China North Industries Group Corporation Co. Ltd Nanjing China

Automatic brain tumor segmentation from multispectral magnetic resonance imaging (MRI) data is an important but a challenging task because of the high diversity in the appearance of tumor tissues among different patients and in many cases similarity with the normal tissues. In this paper, we propose a fully automatic technique for brain tumor segmentation from multispectral human brain MRIs. We first use the intensities of different patches in multispectral MRIs to represent the features of both normal and abnormal tissues and generate a dictionary for following tissue classification. Then, the sparse representation classification (SRC) is applied to classify the brain tumor and normal brain tissue in the whole image. At last, the Markov random field (MRF) regularization introduces spatial constraints to the SRC to take into account the pair-wise homogeneity in terms of classification labels and multispectral voxel intensities. Our method was evaluated on 20 multi-modality patient datasets with competitive segmentation results. ©, 2015 SERSC.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

引用

BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

来源：评论

学校读者我要写书评

暂无评论

MAMMOTH:A MASSIVE-SCALE EMULATION PLATFORM FOR INTERNET OF THINGS

MAMMOTH:A MASSIVE-SCALE EMULATION PLATFORM FOR INTERNET OF T...

引用

2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems

作者： Vilen Looga Zhonghong Ou Yang Deng Antti Yl-Jski Data Communications Software Lab Department of Computer Science and EngineeringSchool of ScienceAalto University

Internet of Things (IoT) is increasingly used in a plethora of fields to enable radically new ways for various purposes, ranging from monitoring the environment to enhancing the wellbeing of human life. With the ever-increasing size of such networks, it is fundamental to understand the issues that come with scaling on different networking layers. A cost-efficient approach to examine large-scale networks is to use simulators or emulators to test the infrastructure and its ability to support the desired applications. In this paper, we investigate and compare the currently available simulation/emulation software. We found out that the current solutions are mostly appropriate for small- and medium-scale emulation, however they are not suitable for large-scale testing that reaches millions of node running concurrently. We then propose a large-scale IoT emulator called MAMMotH and present a brief overview of its design. Finally we discuss some of the current issues and future directions, e.g. radio link simulation.

关键词： Internet of Things (IoT) Large-scale emulation Simulation

来源：评论

学校读者我要写书评

暂无评论

Access control requirements for processing electronic health records

Access control requirements for processing electronic health...

引用

5th International Conference on Business Process Management

作者： Alhaqbani, Bandar Fidge, Colin Information Security Institute Queensland University of Technology Brisbane Australia School of Software Engineering and Data Communications Queensland University of Technology Brisbane Australia

ISBN: (纸本)9783540782377

There is currently a strong focus worldwide on the potential of large-scale Electronic Health Record systems to cut costs and improve patient outcomes through increased efficiency. A number of countries are developing nationwide EHR systems to aggregate services currently provided by isolated Electronic Medical Record databases. However, such aggregation introduces new risks for patient privacy and data security, both by linking previously-separate pieces of information about an individual, and by creating single access points to a wide range of personal data. It is thus essential that new access control policies and mechanisms are devised for federated Electronic Health Record systems, to ensure not only that sensitive patient data is accessible by authorized personnel only, but also that it is available when needed in life-critical situations. Here we review the traditional security models for access control, Discretionary Access Control, Mandatory Access Control and Role-Based Access Control, and use a case study to demonstrate that no single one of them is sufficient in a federated healthcare environment. We then show how the required level of data security can be achieved through a judicious combination of all three mechanisms.

关键词： Access control

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：