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A General Global and Local Pre-Training Framework for 3D Medical Image Segmentation

IEEE Journal of Biomedical and Health Informatics 2023年 PP卷 1-9页

作者： Ju, Jianguo Guan, Ziyu Lei, Hao Qiu, Dandan Zhang, Haoming Chen, Long xie, Fei Zhao, Wei School of Information Science and Technology Northwest University Xi'an China Powerchina Northwest Engineering Corporation Limited Xi'an China School of Artificial Intelligence Xi'an University of Posts & Telecommunications Xi'an China School of AOAIR Xidian University Xi'an China Computer Science and Technology Xidian University Xi'an China

Accurate target segmentation from computed tomography (CT) scans is crucial for surgical robots to perform clinical surgeries successfully. However, the lack of medical image data and annotations has been the biggest obstacle to learning robust medical image segmentation models. Self-supervised learning can effectively address this problem by providing a strategy to pre-train a model with unlabeled data, and then fine-tune downstream tasks with limited labeled data. Existing self-supervised methods fail to simultaneously utilize the abundant global anatomical structure information and local feature differences in medical imaging. In this work, we propose a new strategy for the pre-training framework, which uses the three-dimensional anatomical structure of medical images and specific task and background cues to segment volumetric medical images with limited annotations. Specifically, we propose (1) learning intrinsic patterns of volumetric medical image structures through multiple sub-tasks, and (2) designing a multi-level background cube contrastive learning strategy to enhance the target feature representation by exploiting the differences between the specific target and background. We conduct extensive evaluations on two publicly available datasets. Under limited annotation settings, the proposed method yields significant improvements compared to other self-supervised learning techniques. The proposed method achieves within 6% of the baseline performance using only five labeled CT volumes for training. Once the paper is online, the code and dataset will be available at https://***/PinkGhost0812/SGL. IEEE

关键词： Image segmentation

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EELMCDA: Combining evolutionary ensemble learning with matrix feature decomposition for predicting circRNA-disease associations

EELMCDA: Combining evolutionary ensemble learning with matri...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Zheng Wang, Lei You, Zhu-Hong Wang, Lei Li, Yang Wang, Zhenyu Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Northwestern Polytechnical University School of Computer Science Xi'an710048 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Lanzhou University of Technology School of Electrical Engineering and Information Engineering Lanzhou730050 China

ISBN: (纸本)9798350386226

Recent studies have indicated that circular RNAs (circRNAs) play a significant role in the diagnosis and treatment of disease. However, the prediction of associations between circRNAs and diseases using conventional biological methods is constrained by numerous factors. In this study, we proposed a novel computational model called EELMCDA that combines evolutionary ensemble learning (EEL) approach and matrix feature decomposition method to predict potential circRNA-disease associations. The model firstly integrates circRNA function information, disease semantic information, and circRNA and disease gaussian interaction profile kernel (GIPK) information into an integrated matrix and constructed the corresponding feature matrix, then uses the matrix feature decomposition algorithm to obtain its important feature, and finally adopted evolutionary ensemble learning module to predict circRNA-disease associations. The average accuracy of the EELMCDA model by 5-fold cross-validation on CircR2Disease, CircAtlasv2.0, Circ2Disease, and CircRNADisease datasets were 92.40%, 92.90%, 88.91%, and 90.74%, respectively. Moreover, in case studies, the 21 of the top 30 circRNA-disease pairs with the highest EELMCDA scores were validated in recent literatures. These results further demonstrate the effectiveness of EELMCDA in predicting circRNA-disease associations. © 2024 IEEE.

关键词： Contrastive Learning

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Transformer-Based Person Re-Identification: A Comprehensive Review

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第7期9卷 1-19页

作者： Sarker, Prodip Kumar Zhao, Qingjie Uddin, Md. Kamal School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Bangladesh

In the evolving landscape of surveillance and security applications, the task of person re-identification(re-ID) has significant importance, but also presents notable difficulties. This task entails the process of accurately matching and identifying persons across several camera views that do not overlap with one another. This is of utmost importance to video surveillance, public safety, and person-tracking applications. However, vision-related difficulties, such as variations in appearance, occlusions, viewpoint changes, cloth changes, scalability, limited robustness to environmental factors, and lack of generalizations, still hinder the development of reliable person re-ID methods. There are few approaches have been developed based on these difficulties relied on traditional deep-learning techniques. Nevertheless, recent advancements of transformer-based methods, have gained widespread adoption in various domains owing to their unique architectural properties. Recently, few transformer-based person re-ID methods have developed based on these difficulties and achieved good results. To develop reliable solutions for person re-ID, a comprehensive analysis of transformer-based methods is necessary. However, there are few studies that consider transformer-based techniques for further investigation. This review proposes recent literature on transformer-based approaches, examining their effectiveness, advantages, and potential challenges. This review is the first of its kind to provide insights into the revolutionary transformer-based methodologies used to tackle many obstacles in person re-ID, providing a forward-thinking outlook on current research and potentially guiding the creation of viable applications in real-world scenarios. The main objective is to provide a useful resource for academics and practitioners engaged in person re-ID. IEEE

关键词： Cameras

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Secure vehicular data communication in Named Data Networking

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Digital Communications and Networks 2023年第1期9卷 203-210页

作者： xiaonan Wang xilan Chen xingwei Wang School of Computer Science and Engineering Changshu Institute of TechnologyChangshu215500China School of Computer Science and Technology Northeastern UniversityShenyang110169China

Vehicular data misuse may lead to traffic accidents and even loss of life,so it is crucial to achieve secure vehicular data *** paper focuses on secure vehicular data communications in the Named Data Networking(NDN).In NDN,names,provider IDs and data are transmitted in plaintext,which exposes vehicular data to security threats and leads to considerable data communication costs and failure *** paper proposes a Secure vehicular Data Communication(SDC)approach in NDN to supress data communication costs and failure *** constructs a vehicular backbone to reduce the number of authenticated nodes involved in reverse *** the ciphtertext of the name and data is included in the signed Interest and Data and transmitted along the backbone,so the secure data communications are *** is evaluated,and the data results demonstrate that SCD achieves the above objectives.

关键词： Named data networking Reverse path Secure data communication

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CrowdCL: Unsupervised Crowd Counting Network via Contrastive Learning

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IEEE Internet of Things Journal 2025年第12期12卷 21704-21719页

作者： Hu, Yingxiang Liu, Yanbo Cao, Guo Wang, Jin Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210096 China Nantong University School of Information Science and Technology 226000 China

With the continuous growth of the population, crowd counting plays a crucial role in intelligent monitoring systems for the Internet of Things (IoT) and smart city development. Accurate monitoring of crowd density not only helps maintain public safety but also effectively promotes the development of smart cities. Currently, supervised crowd counting techniques have made significant progress in improving accuracy, but these methods rely on expensive manual annotations and have limited generalization performance. To address these challenges, this paper proposes an unsupervised crowd counting network based on contrastive learning, named CrowdCL. CrowdCL primarily leverages image-image contrastive learning and text-image contrastive learning to achieve unsupervised crowd counting. Specifically, in image-image contrastive learning, we strengthen the network's ability to distinguish crowd features by designing progressive occlusion strategies and patch matching strategies, effectively differentiating crowd information from background information. In text-image contrastive learning, we construct ordered textual prompts to match ordered feature maps and use modality matching loss (Lm) to guide the image encoder. Additionally, to reduce the loss of fine details and alleviate the interference of complex backgrounds, we design a coarse-grained filtering strategy during the testing phase, assigning higher weights to crowd patches with greater potential. Experiments on multiple public datasets show that CrowdCL not only achieves outstanding performance but also outperforms some fully supervised methods in cross-dataset testing. © 2025 IEEE.

关键词： Contrastive Learning

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Bamboo Fiber Modified Asphalt Mixture Proportion Design and Road Performances Based on Response Surface Method

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Journal of Wuhan university of technology(Materials science) 2023年第1期38卷 156-170页

作者：李海滨 SUN Jianmei WANG Sirui ZHANG Mingming HU Yihong SHENG Yanping School of Architecture and Civil Engineering Xi’an University of Science and TechnologyXi’an 710054China Shaanxi Transportation Holding Group Co. Ltd.Xi’an 710064China School of Material Science and Engineering Chang’an UniversityXi’an 710064China

In order to comprehensively utilize the remaining bamboo residue of bamboo products,this paper presents a research on recycling the bamboo fibers from bamboo residue for improving the performance of the asphalt *** of all,the basic performance parameters of sinocalamus affinis fiber,phyllostachys pubescens fiber,green bamboo fiber were tested and analyzed,and the optimal content and length were put ***,the mix ratio design of the bamboo fiber modified asphalt mixture was further designed through the response surface method,and was verified the rationality of the mix ***,the mixture specimens were made according to the experimental design mix ratio,and the high temperature,low temperature performance and moisture susceptibility of the bamboo fiber modified mixtures asphalt were *** results showed that the high temperature performance,low temperature performance and moisture susceptibility of bamboo fiber modified asphalt mixtures were improved compared with the performance of SBS modified asphalt *** the length of bamboo fiber is 7.25 mm and the content of 0.22%,the road performance of the asphalt mixture was ***,the decomposition of bamboo residue into bamboo fiber and its application in asphalt pavement can improve the reuse of bamboo waste,with remarkable environmental benefits and great promotion value.

关键词： bamboo fiber response surface method asphalt mixture proportion design road performances bamboo waste

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Robust federated contrastive recommender system against targeted model poisoning attack

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science China(Information sciences) 2025年第4期68卷 50-65页

作者： Wei YUAN Chaoqun YANG Liang QU Guanhua YE Quoc Viet Hung NGUYEN Hongzhi YIN School of Electrical Engineering and Computer Science The University of Queensland School of Information and Communication Technology Griffith University Deep Neural Computing Company Limited

Federated recommender systems(FedRecs) have garnered increasing attention recently, thanks to their privacypreserving benefits. However, the decentralized and open characteristics of current FedRecs present at least two ***, the performance of FedRecs is compromised due to highly sparse on-device data for each client. Second, the system's robustness is undermined by the vulnerability to model poisoning attacks launched by malicious users. In this paper, we introduce a novel contrastive learning framework designed to fully leverage the client's sparse data through embedding augmentation, referred to as CL4FedRec. Unlike previous contrastive learning approaches in FedRecs that necessitate clients to share their private parameters, our CL4FedRec aligns with the basic FedRec learning protocol, ensuring compatibility with most existing FedRec implementations. We then evaluate the robustness of FedRecs equipped with CL4FedRec by subjecting it to several state-of-the-art model poisoning attacks. Surprisingly, our observations reveal that contrastive learning tends to exacerbate the vulnerability of FedRecs to these attacks. This is attributed to the enhanced embedding uniformity, making the polluted target item embedding easily proximate to popular items. Based on this insight, we propose an enhanced and robust version of CL4FedRec(rCL4FedRec) by introducing a regularizer to maintain the distance among item embeddings with different popularity levels. Extensive experiments conducted on four commonly used recommendation datasets demonstrate that rCL4FedRec significantly enhances both the model's performance and the robustness of FedRecs.

关键词： federated recommender system contrastive learning model poisoning attack and defense

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Research Progress in Solar Flare Prediction Methods

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Research in Astronomy and Astrophysics 2025年第3期25卷 280-309页

作者： Ke Han Zhen Liu xian-Yi Zhao Yi-Fei Li De-Quan Zheng Jie Wan School of Computer and Information Engineering Harbin University of Commerce Faculty of Computing Harbin Institute of Technology School of Energy Science and Engineering Harbin Institute of Technology

Solar flares are one of the strongest outbursts of solar activity,posing a serious threat to Earth’s critical infrastructure,such as communications,navigation,power,and ***,it is essential to accurately predict solar flares in order to ensure the safety of human ***,the research focuses on two directions:first,identifying predictors with more physical information and higher prediction accuracy,and second,building flare prediction models that can effectively handle complex observational *** terms of flare observability and predictability,this paper analyses multiple dimensions of solar flare observability and evaluates the potential of observational parameters in *** flare prediction models,the paper focuses on data-driven models and physical models,with an emphasis on the advantages of deep learning techniques in dealing with complex and high-dimensional *** reviewing existing traditional machine learning,deep learning,and fusion methods,the key roles of these techniques in improving prediction accuracy and efficiency are *** prevailing challenges,this study discusses the main challenges currently faced in solar flare prediction,such as the complexity of flare samples,the multimodality of observational data,and the interpretability of *** conclusion summarizes these findings and proposes future research directions and potential technology advancement.

关键词： Sun: activity Sun: flares (Sun:) sunspots Sun: magnetic fields magnetohydrodynamics (MHD)

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Structured Light Center Extraction Study with Multiple Attention Mechanisms

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第4期51卷 437-446页

作者： Sun, Hang Zhou, Ziwei School of Computer and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer and Software Engineering University of Science and Technology Liaoning Anshan114051 China

The integration of deep learning with conventional structured light center extraction techniques improves the accuracy of extracting structural gold centers. The method is divided into three steps. The initial step involves calibration, which aims to establish a correlation between image coordinates and world coordinates. The subsequent stage involves identifying the laser fringe area. This study employs a self-designed Multi-Att DeepLabV3+ encoder-decoder neural network architecture to extract the laser fringe region. The self-designed SE-ResSkipNet module is incorporated into the structure as the backbone. The decoder utilizes a parallel alternating dual attention mechanism. The third step involves extracting the center of the laser fringe utilizing the Steger algorithm, which is based on a Hessian matrix. Conducting experimental validation on a laser image dataset that is open-source. The experimental resluts indicate that the network architecture's mIoU, fwIoU, Acc, and Acc class evaluation metrics for complex laser fringe segmentation have shown improvements of 4.22%, 0.67%, 0.33%, and 4.96%, respectively. This algorithm demonstrates superior accuracy compared to other algorithms in laser fringe segmentation, playing a crucial role in the subsequent processes of 3D reconstruction and 3D measurement. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Decoding

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High-areal-capacity/power lithium metal microbattery configuration based on the mechanically flexible,ultra-lightweight,nanocellulose framework

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Nano Research 2024年第9期17卷 8155-8162页

作者： Shaowen Li Ting Zhao Helin Wang Zhiqiao Wang Min Zhang Ahu Shao Jiacheng Liu Zhaohui Wang Yue Ma State Key Laboratory of Solidification Processing Centre for Nano Energy MaterialsSchool of Materials Science and EngineeringNorthwestern Polytechnical UniversityXi'an 710072China College of Materials Science and Engineering Hunan UniversityChangsha 410082China The Faculty of Printing Packaging Engineering and Digital Media TechnologyXi'an University of TechnologyXi'an 710048China

The ubiquitous implementation of integrated microelectronics requires the on-chip power sources featured with the lightweight configuration design,high areal-capacity-loadings as well as facile reaction kinetics that beyond the current available microbattery ***,this study constructs a mechanically flexible,nanocellulose fiber(NCF)reinforced microbattery configuration,which consists of metal-organic frameworks(ZIF-8)modified NCF as the separator(MOF@NCF),the carbonized MOF@NCF as the metallic deposition substrate(c-MOF@NCF)as well as gradient-structured LiFePO4 particles infiltrated in the NCF matrix(LFP@NCF)as the *** film-stacked,integrated NCF-based microbattery prototype not only achieves the facile reaction kinetics with homogenized,dendrite-free Li metal deposition at high-capacity-loadings(2 mAh·cm^(-2)),but also eliminates the necessary use of metallic current collector to maximize the electroactive mass ratio,which therefore enables the high energy density of 6.8 mWh·cm^(-2)at the power output of 1.36 mW·cm^(-2)as well as the robust cyclability upon various geometric flexing *** study presents a quantum leap towards the facile reaction kinetics and multi-scale interfacial stability for the flexible microbattery construction that based on the sustainable utilization of bio-scaffolds.

关键词： nanocellulose microbattery mechanical flexible ultra-lightweight high-areal-capacity

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