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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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Fault diagnosis design based on qualitative simulation

Fault diagnosis design based on qualitative simulation

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International Conference on Intelligent Control and Information Processing (ICICIP)

作者： Chenxi Shao Zhenzhong Shao Juntao Zhang Fanzhou Bai College of Computer Science & Technology University of Science and Technology of China Anhui Province Key Laboratory of Software in Computing and Communication Hefei Anhui China Jianghuai Automobile Co. Ltd. Hefei China College of Computer Science & Technology University of Science and Technology of China Hefei

Based on the QSIM reasoning of Kuipers, comparative constraint is presented to reduce the diagnosis space. The transfer regulation of qualitative constrain is used in the simulation and reasoning of system diagnosis. This arithmetic takes observed faulty state as the beginning, to diagnosis the discrepancy of variable and location of the causation according to the transfer regulation of qualitative constraint, reasons from faulty diagnosed to examine the result and delete the redundancy of diagnosis result. In the example of Condensation refrigeration system, the constraint relation is built according to qualitative difference equation. Aim at the bad efficiency of refrigeration, the mixture of air or Freon superfluously is diagnosed as the result faulty source, is consistent with the factual system.

关键词： Circuit faults Mathematical model Differential equations Algebra Valves Cognition Educational institutions

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Dynamic protocol conformance test method based on local error diagnostic information

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Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications 2007年第1期30卷 140-144页

作者： Lin, Hua-Hui Zhou, Hao Pan, Quan-Ke Zhao, Bao-Hua Department of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Anhui Province Key Laboratory of Software in Computing and Communication Hefei 230027 China

To enhance the reliability and the fault coverage of the test results, a local error diagnostic mechanism was introduced into dynamic protocol conformance test, a new dynamic test method was proposed, whose principle and correctness was analyzed on determinate FSM model. The new method took full advantage of the dynamic information generated during test to avoid the incorrect locating of local error. Also a local error diagnostic method was presented on error sub-sequence, which was not restricted to single error assumption and more practical. The experimental results showed that the method can effectively avoid the descending of the test reliability and give better fault coverage.

关键词： Transmission control protocol

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Blockchain-Based Efficiently Privacy-Preserving Federated Learning Framework Using Shamir Secret Sharing

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Fu, Xiya Xiong, Ling Li, Fagen Yang, Xingchun Xiong, Naixue School of Computer and Software Engineering Xihua University Chengdu China University of Electronic Science and Technology of China Chengdu China Intelligent Policing Key Laboratory of Sichuan Province Chengdu China College of Intelligence and Computing Tianjin University Tianjin China

Federated learning, an emerging distributed learning paradigm, offers significant advantages and holds promise for addressing trust issues, breaking down data silos, and enabling active data sharing in the realm of consumer electronics. However, traditional federated learning faces critical security and privacy challenges in this domain, including single-point attacks, inference attacks, and poisoning attacks. To address these pressing security concerns, this paper proposes a decentralized federated learning framework focusing on security, reliability, and efficiency, tailored to meet the sustainability demands of consumer electronics. The proposed framework is founded upon masking and secret sharing techniques, establishing an emphatic privacy-preserving federated learning framework that ensures the security of gradient data and robustness against participant dropouts. Additionally, we actively motivate high-quality participants to collaborate by incorporating an incentive mechanism. Building upon the enhancement of existing federated learning approaches reliant on masking techniques, the method outlined in this paper significantly reduces communication overhead while preserving accuracy. Empirical research results comprehensively substantiate the superiority of this approach. Furthermore, compared to prevalent blockchain-based federated learning methods, our approach makes noteworthy strides in accuracy and efficiency. IEEE

关键词： Blockchain

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OSAF_e: One-Stage Anchor Free Object Detection Method Considering Effective Area 9th

OSAF_e: One-Stage Anchor Free Object Detection Method Consid...

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9th International Conference on Artificial Intelligence and Mobile Services, AIMS 2020, held as part of the Services Conference Federation, SCF 2020

作者： Zhang, Yong Zhang, Lizong Rao, Zhihong Duan, Guiduo Wang, Chunyu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China China Electronic Technology Cyber Security Co. Ltd. Chengdu610000 China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu611731 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9783030596040

The task of object detection is to identify the bounding box of the object and its corresponding category in images. In this paper, we propose a new one-stage anchor free object detection algorithm OSAF_e, with the consideration of effective mapping area. A feature extraction network is used to obtain high level feature, and the true bounding box of the object in the original image is mapped to the grid of feature map, in order to perform category prediction and bounding box regression. The proposed algorithm is evaluated with the Pascal Voc dataset, and the experiments indicate that it has a better result. © 2020, Springer Nature Switzerland AG.

关键词： Object detection

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ThyroidNet:A Deep Learning Network for Localization and Classification of Thyroid Nodules

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 361-382页

作者： Lu Chen Huaqiang Chen Zhikai Pan Sheng Xu Guangsheng Lai Shuwen Chen Shuihua Wang Xiaodong Gu Yudong Zhang Ultrasonic Department Zhongda Hospital Affiliated to Southeast UniversityNanjing210009China School of Physics and Information Engineering Jiangsu Second Normal UniversityNanjing211200China School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia State Key Laboratory of MillimeterWaves Southeast UniversityNanjing210096China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Jiangsu Second Normal UniversityNanjing211200China School of Software Engineering Quanzhou Normal UniversityQuanzhou362000China Department of Biological Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou215123China

Aim:This study aims to establish an artificial intelligence model,ThyroidNet,to diagnose thyroid nodules using deep learning techniques ***:A novel method,ThyroidNet,is introduced and evaluated based on deep learning for the localization and classification of thyroid ***,we propose the multitask TransUnet,which combines the TransUnet encoder and decoder with multitask ***,we propose the DualLoss function,tailored to the thyroid nodule localization and classification *** balances the learning of the localization and classification tasks to help improve the model’s generalization ***,we introduce strategies for augmenting the ***,we submit a novel deep learning model,ThyroidNet,to accurately detect thyroid ***:ThyroidNet was evaluated on private datasets and was comparable to other existing methods,including U-Net and *** results show that ThyroidNet outperformed these methods in localizing and classifying thyroid *** achieved improved accuracy of 3.9%and 1.5%,***:ThyroidNet significantly improves the clinical diagnosis of thyroid nodules and supports medical image analysis *** research directions include optimization of the model structure,expansion of the dataset size,reduction of computational complexity and memory requirements,and exploration of additional applications of ThyroidNet in medical image analysis.

关键词： ThyroidNet deep learning TransUnet multitask learning medical image analysis

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A probabilistic group reverse k-nearest-neighbor query in sensor networks 1

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7th China Conference of Wireless Sensor Networks, CWSN 2013

作者： Zhu, Jinghua Kan, Bin Liu, Yong Wang, Tong Pan, Liqiang Liu, Dan School of Computer Science and Technology Heilongjiang University Harbin 150080 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin 150080 China College of Information and Communication Engineering Harbin Engineering University Harbin 150001 China School of Computer and Science Technology Harbin School of Technology Harbin 150001 China School of Computer Science Northeast Forestry University Harbin 150001 China

ISBN: (数字)9783642541742

ISBN: (纸本)9783642541735

Group Reverse k-Nearest-Neighbor Query (GRkNN) can be used to evaluate the influences of query objects and has wide applications. Traditional algorithms are not able to obtain satisfied results for uncertain sensor network data. A probabilistic threshold group reverse k nearest neighbor query (PT-GRkNN) is proposed in this paper. PT-GRkNN exploits filter-refine framework, geometric filter and probability filter are proposed to shrink the search space, the final query results can be obtained by refine phase. Experimental results show that the efficiency of PT-GRkNN is better than the existing methods. © 2014 Springer-Verlag Berlin Heidelberg.

关键词： Sensor networks

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Confining Wi-Fi Coverage: A Crowdsourced Method Using Physical Layer Information

Confining Wi-Fi Coverage: A Crowdsourced Method Using Physic...

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IEEE communications Society Conference on Sensor and Ad Hoc communications and Networks (SECON)

作者： Bingxian Lu Zhicheng Zeng Lei Wang Brian Peck Daji Qiao Michael Segal Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology China Department of Electrical and Computer Engineering Iowa State University USA Communication Systems Engineering Department Ben-Gurion University of the Negev Israel

ISBN: (纸本)9781509017331

Many small businesses and public areas offer free Wi-Fi access, but may wish to restrict network access only to their customers or patrons inside the physical property. Unfortunately, due to the nature of wireless networks, this is difficult to accomplish. We develop and implement CLAC, a Crowdsourced Location aware Access Control scheme using physical layer information to address this challenge. It crowdsources both channel state information (CSI) and received signal strength (RSS) of already validated users to classify future users. We propose and use two CSI metrics in CLAC: CSI Cross-Antenna Stability Metric and CSI Cross-Frame Stability Metric, which summarize well the spatial and temporal CSI characteristics respectively. CLAC is evaluated in an office and a classroom. Evaluation results show that CLAC performs well in both environments, allowing most valid users inside the area to access the network, while the chance that invalid users outside the boundary may access the network is small.

关键词： Training Antennas IEEE 802.11 Standard Monitoring Measurement Signal to noise ratio Indexes

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The Library for Hadoop Deflate Compression Based on FPGA Accelerator with Load Balance

The Library for Hadoop Deflate Compression Based on FPGA Acc...

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IEEE International Conference on Parallel and Distributed computing, Applications and Technologies (PDCAT)

作者： Haixin Du Jiankui Zhang Shihao Sha Cai Ye Qiuming Luo College of Computer Science and Software Engineering Shenzhen University NHPCC/Guangdong Key Laboratory of popular HPC and Guangdong Province Engineering Center of China-made High Performance Data Computing System Shenzhen China

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

Hadoop application will produce lots of intermediate results in the map/reduce process that requires disk I/O and network transmission. By compressing the large-scale data of intermediate result, it will greatly improve disk access efficiently and reduce program run time. Hardware-accelerated solutions have become more desirable. This paper design a multi-FPGA compression accelerator on the Hadoop platform, and the system performance analysis compared with a software-only solution that mainly uses CPU to processing. The testing programs are zpipe, TestDFSIO and Terasort. In contrast with the software-only solution. The max speedup of zpipe is 6.55X (single FPGA) and 10.24X (dual FPGA), the max speedup of TestDFSIO is 6.28X (single FPGA) and 6.28X (dual FPGA), and the max speedup of Terasort application is up to 3.25X(single FPGA) and 3.35X(dual FPGA).

关键词： Handheld computers Conferences Computer science software engineering Distributed databases Indexes

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XML based workflow definition language design

XML based workflow definition language design

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IEEE International Conference on software Engineering and Service Sciences (ICSESS)

作者： Yun Feng Yu Xiang Zhou Key Laboratory of Software Engineering of Yunnan Province Department of Computer Science Zunyi Normal College 563002 China Department of Computing Imperial College London SW7 2AZ UK

Workflow is a technology which software system uses highly frequent. A good workflow system can replace the traditional manual way document circulation and greatly simplify office process. It has become an indispensable constituent part of the business or government software. For the convenience of workflow converting to code, workflow process definition language is designed combined with web technology XML.

关键词： XML Shape Receivers

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