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arXiv 2024年

作者： Lyu, Jun Qin, Chen Wang, Shuo Wang, Fanwen Li, Yan Wang, Zi Guo, Kunyuan Ouyang, Cheng Tänzer, Michael Liu, Meng Sun, Longyu Sun, Mengting Li, Qin Shi, Zhang Hua, Sha Li, Hao Chen, Zhensen Zhang, Zhenlin Xin, Bingyu Metaxas, Dimitris N. Yiasemis, George Teuwen, Jonas Zhang, Liping Chen, Weitian Pang, Yanwei Liu, Xiaohan Razumov, Artem Dylov, Dmitry V. Dou, Quan Yan, Kang Xue, Yuyang Du, Yuning Dietlmeier, Julia Garcia-Cabrera, Carles Hemidi, Ziad Al-Haj Vogt, Nora Xu, Ziqiang Zhang, Yajing Chu, Ying-Hua Chen, Weibo Bai, Wenjia Zhuang, Xiahai Qin, Jing Wu, Lianmin Yang, Guang Qu, Xiaobo Wang, He Wang, Chengyan Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School 399 Revolution Drive BostonMA02215 United States Department of Electrical and Electronic Engineering & I-X Imperial College London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Bioengineering & I-X Imperial College London LondonW12 7SL United Kingdom Cardiovascular Magnetic Resonance Unit Royal Brompton Hospital Guy’s and St Thomas’ NHS Foundation Trust LondonSW3 6NP United Kingdom Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361102 China Department of Computing Department of Brain Sciences Imperial College London LondonSW7 2AZ United Kingdom Human Phenome Institute Fudan University 825 Zhangheng Road Pudong New District Shanghai201203 China Department of Radiology Zhongshan Hospital Fudan University Shanghai China Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Department of Computer Science Rutgers University PiscatawayNJ08854 United States AI for Oncology Netherlands Cancer Institute Plesmanlaan 121 Amsterdam1066 CX Netherlands Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Hong Kong TJK-BIIT Lab School of Electrical and Information Engineering Tianjin University Tianjin300072 China Skolkovo Institute Of Science And Technology Center for Artificial Intelligence Technology 30/1 Bolshoy blvd. Moscow121205 Russia Department of Biomedical Engineering University of Virginia

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart’s structure, function, and tissue characteristics with high-resolution spatial-temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on Medical Image Computing and computer-Assisted Intervention (MICCAI). CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techn

关键词： Mapping

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Preface

Lecture Notes in Electrical Engineering

引用

Lecture Notes in Electrical engineering 2022年 808 LNEE卷 v页

作者： Liu, Qi Liu, Xiaodong Chen, Bo Zhang, Yiming Peng, Jiansheng School of Computer and Software Nanjing University of Information Science and Technology Nanjing Jiangsu China School of Computing Edinburgh Napier University Edinburgh United Kingdom State Key Laboratory of Radar Signal Processing Xidian University Xi’an Shaanxi China School of Civil Engineering and Transportation Hebei University of Technology Tianjin Tianjin China Hechi Universtiy Guangxi Hechi China

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On the k-error linear complexity of binary sequences derived from the discrete logarithm in finite fields

arXiv

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arXiv 2019年

作者： Chen, Zhixiong Wang, Qiuyan Provincial Key Laboratory of Applied Mathematics Putian University Putian Fujian351100 China School of Computer Science and Software Engineering Tianjin Polytechnic University Tianjin300387 China

Let q = pr be a power of an odd prime p. We study binary se- quences σ = (σ0, σ1, ⋯) with entries in {0, 1} defined by using the quadratic character x of the finite field Fq: σn ={0, if n = 0,(1 - x(ζn))/2, if 1 ... 详细信息

关键词： Binary sequences

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Goal-Aware RSS for Complex Scenarios via Program Logic

arXiv

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arXiv 2022年

作者： Hasuo, Ichiro Eberhart, Clovis Haydon, James Dubut, Jérémy Bohrer, Rose Kobayashi, Tsutomu Pruekprasert, Sasinee Zhang, Xiao-Yi Pallas, Erik André Yamada, Akihisa Suenaga, Kohei Ishikawa, Fuyuki Kamijo, Kenji Shinya, Yoshiyuki Suetomi, Takamasa Tokyo101-8430 Japan Dept. Computer Science Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609-2280 United States Inst. Software & Systems Engineering University of Augsburg Universitätstraße 6a AugsburgD-86135 Germany Cyber Physical Security Research Center AIST Aomi 2-4-7 Tokyo135-0064 Japan Graduate School of Informatics Kyoto University Kyoto606-8501 Japan Mazda Motor Corporation Fuchu730-8670 Japan SOKENDAI The Graduate University for Advanced Studies Japan Tokyo Japan

We introduce a goal-aware extension of responsibility-sensitive safety (RSS), a recent methodology for rule-based safety guarantee for automated driving systems (ADS). Making RSS rules guarantee goal achievement—in addition to collision avoidance as in the original RSS—requires complex planning over long sequences of manoeuvres. To deal with the complexity, we introduce a compositional reasoning framework based on program logic, in which one can systematically develop RSS rules for smaller subscenarios and combine them to obtain RSS rules for bigger scenarios. As the basis of the framework, we introduce a program logic dFHL that accommodates continuous dynamics and safety conditions. Our framework presents a dFHL-based workflow for deriving goal-aware RSS rules;we discuss its software support, too. We conducted experimental evaluation using RSS rules in a safety architecture. Its results show that goal-aware RSS is indeed effective in realising both collision avoidance and goal achievement. © 2022, CC BY-NC-ND.

关键词： computer circuits

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Woven fabric defect detection based on convolutional neural network for binary classification

Woven fabric defect detection based on convolutional neural ...

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Artificial Intelligence on Fashion and Textiles Conference, AIFT 2018

作者： Gao, Can Zhou, Jie Wong, Wai Keung Gao, Tianyu College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Institute of Textiles and Clothing The Hong Kong Polytechnic University Kowloon Hong Kong School of Minerals Processing and Bioengineering Central South University Changsha410083 China

ISBN: (纸本)9783319996943

Fabric defect detection plays an important role in the textile industry. However, this problem is very challenging because of the variability of texture and diversity of defect. In this paper, we investigate the problem of woven fabric defect detection using deep learning. A convolutional neural network with multi-convolution and max-pooling layers is proposed. Moreover, a high-quality database, which covers the common defects in woven fabric with solid color, is built. The experiments conducted on the database indicate that the proposed model could obtain the overall detection accuracy 96.52%, which shows the potential of the model in practical application. © Springer Nature Switzerland AG 2019.

关键词： Defects

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GAMMA Challenge: Glaucoma grAding from Multi-Modality imAges

arXiv

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arXiv 2022年

作者： Wu, Junde Fang, Huihui Li, Fei Fu, Huazhu Lin, Fengbin Li, Jiongcheng Huang, Yue Yu, Qinji Song, Sifan Xu, Xinxing Xu, Yanyu Wang, Wensai Wang, Lingxiao Lu, Shuai Li, Huiqi Huang, Shihua Lu, Zhichao Ou, Chubin Wei, Xifei Liu, Bingyuan Kobbi, Riadh Tang, Xiaoying Lin, Li Zhou, Qiang Hu, Qiang Bogunović, Hrvoje Orlando, José Ignacio Zhang, Xiulan Xu, Yanwu State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China Singapore School of Informatics Xiamen University Xiamen China Shanghai Jiao Tong University Shanghai China Xi'an Jiaotong-Liverpool University Suzhou China Institute of High Performance Computing A*STAR Singapore Institute of Biomedical Engineering Chinese Academy of Medical Sciences Peking Union Medical College Tianjin China School of Medical Technology Beijing Institute of Technology Beijing China Department of Computing Hong Kong Polytechnic University Hong Kong Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Weizhi Medical Technology Company Suzhou China École de Technologie Supérieure Montreal Montreal Canada DIAGNOS Inc. Quebec Canada Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Technology Co. Ltd. Shanghai China School of Information and Electronics Beijing Institute of Technology Beijing China Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology Medical University of Vienna Austria Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina

Glaucoma is a chronic neuro-degenerative condition that is one of the world's leading causes of irreversible but preventable blindness. The blindness is generally caused by the lack of timely detection and treatment. Early screening is thus essential for early treatment to preserve vision and maintain life quality. Color fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both imaging modalities have prominent biomarkers to indicate glaucoma suspects, such as the vertical cup-to-disc ratio (vCDR) on fundus images and retinal nerve fiber layer (RNFL) thickness on OCT volume. In clinical practice, it is often recommended to take both of the screenings for a more accurate and reliable diagnosis. However, although numerous algorithms are proposed based on fundus images or OCT volumes for the automated glaucoma detection, there are few methods that leverage both of the modalities to achieve the target. To fulfill the research gap, we set up the Glaucoma grAding from Multi-Modality imAges (GAMMA) Challenge to encourage the development of fundus & OCT-based glaucoma grading. The primary task of the challenge is to grade glaucoma from both the 2D fundus images and 3D OCT scanning volumes. As part of GAMMA, we have publicly released a glaucoma annotated dataset with both 2D fundus color photography and 3D OCT volumes, which is the first multi-modality dataset for machine learning based glaucoma grading. In addition, an evaluation framework is also established to evaluate the performance of the submitted methods. During the challenge, 1272 results were submitted, and finally, ten best performing teams were selected for the final stage. We analyze their results and summarize their methods in the paper. Since all the teams submitted their source code in the challenge, we conducted a detailed ablation study to verify the effectiveness of the particular modules proposed. Finally, we identify the proposed techniqu

关键词： Grading

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Local and global structure-aware entropy regularized mean teacher model for 3d left atrium segmentation 23rd

Local and global structure-aware entropy regularized mean te...

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Hang, Wenlong Feng, Wei Liang, Shuang Yu, Lequan Wang, Qiong Choi, Kup-Sze Qin, Jing School of Computer Science and Technology Nanjing Tech University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Department of Radiation Oncology Stanford University StanfordCA94305 United States CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China School of Nursing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783030597092

Emerging self-ensembling methods have achieved promising semi-supervised segmentation performances on medical images through forcing consistent predictions of unannotated data under different perturbations. However, the consistency only penalizes on independent pixel-level predictions, making structure-level information of predictions not exploited in the learning procedure. In view of this, we propose a novel structure-aware entropy regularized mean teacher model to address the above limitation. Specifically, we firstly introduce the entropy minimization principle to the student network, thereby adjusting itself to produce high-confident predictions of unannotated images. Based on this, we design a local structural consistency loss to encourage the consistency of inter-voxel similarities within the same local region of predictions from teacher and student networks. To further capture local structural dependencies, we enforce the global structural consistency by matching the weighted self-information maps between two networks. In this way, our model can minimize the prediction uncertainty of unannotated images, and more importantly that it can capture local and global structural information and their complementarity. We evaluate the proposed method on a publicly available 3D left atrium MR image dataset. Experimental results demonstrate that our method achieves outstanding segmentation performances than the state-of-the-art approaches in scenes with limited annotated images. © Springer Nature Switzerland AG 2020.

关键词： Forecasting

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Dual-enhanced word representations based on knowledge base

Dual-enhanced word representations based on knowledge base

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2018 ISWC Posters and Demonstrations, Industry and Blue Sky Ideas Tracks, ISWC-P and D-Industry-BlueSky 2018

作者： He, Fangyuan Zhou, Yi Zhang, Haodi Feng, Zhiyong School of Computer Science and Technology Tianjin University Belgium School of Computing Engineering and Mathematics Western Sydney University Australia College of Computer Science and Software Engineering Shenzhen University Belgium School of Software Tianjin University Belgium

In this paper, we propose an approach for enhancing word representations twice based on large-scale knowledge bases. In the first layer of enhancement, we use the knowledge base as another contextual form corresponding to the corpus and add it to the training of distributed semantics including neural network based and matrix-based. In the second layer, we utilize local features of the knowledge base to enhance the word representations by mutual reinforcement between the keyword and the strongly associated words. We evaluate our approach not only on the well-known datasets but also on a brand-new dataset, IQ-Synonym-323. The results show that our approach compares favorably to other word representations. © 2018 CEUR-WS. All rights reserved.

关键词： Semantics

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Passivity of Reaction-Diffusion Neural Networks Via Sampled-Data Control

Passivity of Reaction-Diffusion Neural Networks Via Sampled-...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Zi-Peng Wang Huai-Ning Wu Wu-Hua Chen Jin-Liang Wang School of Electrical Engineering University of Jinan Jinan China Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beihang University Beijing China College of Mathematics and Information Science Guangxi University Nanning China School of Computer Science and Software Engineering Tianjin Polytechnic University Tianjin China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

A sampled-data (SD) control is introduced for passivity of reaction-diffusion neural networks (RDNNs). With an appropriate Lyapunov functional (LF), a SD control design is developed based on linear matrix inequalities to guarantee the passivity of closed-loop RDNNs. Lastly, we provide a numerical example to support the presented method.

关键词： Artificial neural networks Linear matrix inequalities Control design Closed loop systems Lyapunov methods Microcontrollers Sampled data systems

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General Decay Synchronization of Multi-Weighted Coupled Reaction-Diffusion Neural Networks with and without Time-Varying Delays

General Decay Synchronization of Multi-Weighted Coupled Reac...

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第三十八届中国控制会议

作者： Jian-Mou Lu Yan-Li Huang Shun-Yan Ren Dong-Fang Liu School of Computer Science and Technology Tianjin Key Laboratory of Optoelectronic Detection Technology and System Tianjin Polytechnic University School of Mechanical Engineering Tianjin Polytechnic University Tianjin Tianhang Wisdom Technology Co. Ltd

This paper investigates the decay synchronization problem of multi-weighted coupled reaction-diffusion neural networks(MWCRDNNs) with and without coupling delays. Firstly, we present the decay synchronization concept based on ψ-type function and ψ-type stability. Secondly, some criteria for decay synchronization of the considered MWCRDNNs are proposed by designing appropriate controllers and using several inequality techniques. Finally, a numerical example is given at the end of this paper to verify the effectiveness and correctness of our obtained results.

关键词： General decay synchronization Multi-Weights Delayed coupling Reaction-Diffusion terms

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