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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Wang, Jiahao Su, Yingxue Zhu, Keying Basarah, Michelle Avery Zhong, Yiheng Liu, Yangchuan Zhao, Yuxuan Stefanidis, Angelos Liu, Jingxin School of Ai and Advanced Computing Xi'an Jiaotong-Liverpool University Suzhou China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences Suzhou China

ISBN: (纸本)9798331520526

Computed Tomography (CT) technology is widely used in clinical diagnosis;however, metal implants often introduce severe artifacts in CT images. To address this issue, metal artifact reduction (MAR) techniques have emerged. In the image domain, existing MAR methods have made some progress, but few have focused on utilizing artifact information to guide models effectively. To bridge this gap, we propose a salience-enhanced metal artifact reduction network. The framework is comprised of two stages: In the first stage, a Siamese network extracts common artifact features from different datasets while distinguishing non-artifact features, and Grad-CAM is utilized to generate heatmaps that highlight salient artifact features. In the second stage, a baseline network based on a transformer module, guided by these salience maps, concentrates on artifact regions for precise artifact correction. We evaluated our model's performance using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The experimental results achieved state-of-the-art performance, demonstrating the efficacy of the proposed method. © 2025 IEEE.

关键词： Attention Guided Computed Tomography Heatmaps Metal Artifacts

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Introducing on-chain graph data to consortium blockchain for commercial transactions

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Frontiers of Computer Science 2024年第2期18卷 255-257页

作者： Pengyi ZHANG Yuchen YUAN Jie SONG Yu GU Qiang QU Yongjie BAI Software College Northeastern UniversityShenyang 110819China School of Computer Science and Engineering Northeastern UniversityShenyang 110819China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Blockchain Lab Huawei Cloud Computing Technologies Co.Ltd.Shenzhen 518101China

1 Introduction A blockchain can be referred to as a distributed ledger[1].Nowadays,according to our previous work[2],the graphrelated blockchain grows efficiently in both academic studies and commercial *** data,algor... 详细信息

关键词： transactions ledge commercial

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SNCA:Semi-Supervised Node Classification for Evolving Large Attributed Graphs

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Big Data Mining and Analytics 2024年第3期7卷 794-808页

作者： Faima Abbasi Muhammad Muzammal Qiang Qu Farhan Riaz Jawad Ashraf Luxembourg Institute of Science and Technology and University of Luxembourg Esch-Sur-Alzette L-4362Luxembourg Department of Computer and Information Sciences Northumbria UniversityNewcastle NE18STUK Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China School of Computer Science University of LincolnLincoln LN67TSUK Faculty of Computing Engineering and MediaDe Montfort UniversityLeicesterLE19BHUK

Attributed graphs have an additional sign vector for each ***,edge signs represent like or dislike relationship between the node *** has applications in domains,such as recommender systems,personalised search,***,limited availability of edge sign information in attributed networks requires inferring the underlying graph embeddings to fill-in the knowledge *** inference is performed by way of node classification which aims to deduce the node characteristics based on the topological structure of the graph and signed interactions between the *** study of attributed networks is challenging due to noise,sparsity,and class imbalance *** this work,we consider node centrality in conjunction with edge signs to contemplate the node classification problem in attributed *** propose Semi-supervised Node Classification in Attributed graphs(SNCA).SNCA is robust to underlying network noise,and has in-built class imbalance handling *** perform an extensive experimental study on real-world datasets to showcase the efficiency,scalability,robustness,and pertinence of the *** performance results demonstrate the suitability of the solution for large attributed graphs in real-world settings.

关键词： attributed networks node classification recommender systems

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Data quantity governance for machine learning in materials science

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National Science Review 2023年第7期10卷 234-250页

作者： Yue Liu Zhengwei Yang Xinxin Zou Shuchang Ma Dahui Liu Maxim Avdeev Siqi Shi School of Computer Engineering and Science Shanghai University Shanghai Engineering Research Center of Intelligent Computing System Australian Nuclear Science and Technology Organisation School of Chemistry The University of Sydney State Key Laboratory of Advanced Special Steel School of Materials Science and Engineering Shanghai University Materials Genome Institute Shanghai University

Data-driven machine learning(ML) is widely employed in the analysis of materials structure-activity relationships,performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate ***,because of the laborious process of materials data acquisition,ML models encounter the issue of the mismatch between a high dimension of feature space and a small sample size(for traditional ML models) or the mismatch between model parameters and sample size(for deep-learning models),usually resulting in terrible ***,we review the efforts for tackling this issue via feature reduction,sample augmentation and specific ML approaches,and show that the balance between the number of samples and features or model parameters should attract great attention during data quantity *** this,we propose a synergistic data quantity governance flow with the incorporation of materials domain *** summarizing the approaches to incorporating materials domain knowledge into the process of ML,we provide examples of incorporating domain knowledge into governance schemes to demonstrate the advantages of the approach and *** work paves the way for obtaining the required high-quality data to accelerate materials design and discovery based on ML.

关键词： machine learning data governance data quantity materials science

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Bioinspired sensing-memory-computing integrated vision systems: biomimetic mechanisms, design principles, and applications

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Science China(Information Sciences) 2024年第5期67卷 5-27页

作者： Yujie HUANG Yinlong TAN Yan KANG Yabo CHEN Yuhua TANG Tian JIANG Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology College of Advanced Interdisciplinary Studies National University of Defense Technology Institute for Quantum Science and Technology College of ScienceNational University of Defense Technology

With the explosion of sensory data in the Internet of Things(Io T) era, conventional machine vision systems are becoming increasingly difficult to meet the requirements of high efficiency, low energy consumption, and low latency due to their inherent shortcomings of separate sensing, memory, and computing units. Inspired by the retina and neuromorphic computing, the sensing-memory-computing integrated vision system(SMCVS) that features low power consumption, low latency, and high parallelism has been considered a promising technology to surpass the von Neumann architecture and realize strong artificial ***, novel materials like two-dimensional semiconductors and quantum dots with novel optoelectronic performance provide hardware carriers for implementing integrated sensing-memory-computing architectures,attracting considerable attention. This paper reviews the recent research progress in bioinspired vision systems in terms of biomimetic mechanisms, design principles, computational architectures, and ***, the biomimetic mechanisms are illustrated to guide the design of high-performance artificial visual perception systems. Then the research progress of optoelectronic-synapse-based bioinspired vision systems in the device principles and applications including image filtering, color recognition, visual adaptation, and motion detection are summarized. Finally, the challenges and future developing directions of the SMCVS are provided regarding bionic application, architecture design, and device fabrication.

关键词： sensing-memory-computing architecture neuromorphic computing retinomorphic device image recognition visual system

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Medical image registration and its application in retinal images:a review

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Visual computing for Industry,Biomedicine,and Art 2024年第1期7卷 142-164页

作者： Qiushi Nie Xiaoqing Zhang Yan Hu Mingdao Gong Jiang Liu Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Singapore Eye Research Institute Singapore 169856Singapore State Key Laboratory of Ophthalmology Optometry and Visual ScienceEye HospitalWenzhou Medical UniversityWenzhou 325027China

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse informa-tion of images,which may be captured under different times,angles,or *** several surveys have reviewed the development of medical image registration,they have not systematically summarized the existing med-ical image registration *** this end,a comprehensive review of these methods is provided from traditional and deep-learning-based perspectives,aiming to help audiences quickly understand the development of medical image *** particular,we review recent advances in retinal image registration,which has not attracted much *** addition,current challenges in retinal image registration are discussed and insights and prospects for future research provided.

关键词： Computer-aided diagnosis Medical image registration Deep learning Generative model Transformer Retina

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Graph Attention Preemptive Traffic Aware Sparse Neural Autoencoder-Based Secured Data Transmission in Cloud computing

Graph Attention Preemptive Traffic Aware Sparse Neural Autoe...

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Data Science and Network Security (ICDSNS), International Conference on

作者： S. Radha S. Jeyalaksshmi School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Chennai India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

With the development of several services, traffic signal preemption is carried out for emergency tasks. For performing workflow intent, cloud systems offer unlimited virtual resources for designing the traffic signal preemption in emergency tasks. While some efforts have been made to implement traffic signal preemption for emergencies, more attention must be given to improving response times. A hybrid method called Graph Attention Preemptive Traffic-aware Sparse Neural Autoencoder (GAPT-SNA) is proposed for secured data transmission in the cloud platform. The GAPT-SNA method is split into the preemptive traffic-aware relocation model and secure data transmission in the CC environment. Initially, the Graph Attention Recurrent Preemptive Relocation model is designed with an Enhanced Healthcare Monitoring System (EHMS) testbed dataset. The decision is forwarded to the discriminator network for scheduling. The Sparse Gradient Neural Auto-encoder is designed to ensure secure data transmission by utilizing preemptive traffic-aware relocated results. The sparse gradient neural function ensures sparsity on prediction tasks for boosting data integrity. Java Software was used to simulate and validate performance metrics, including response time, Throughput, data confidentiality, and data integrity, using the EHMS testbed dataset.

关键词： Cloud computing Schedules Data integrity Roads Medical services Predictive models Throughput

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Correcting on-chip distortion of control pulses with silicon spin qubits

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Chinese Physics B 2025年第1期34卷 265-271页

作者： Ming Ni Rong-Long Ma Zhen-Zhen Kong Ning Chu Wei-Zhu Liao Sheng-Kai Zhu Chu Wang Gang Luo Di Liu Gang Cao Gui-Lei Wang Hai-Ou Li Guo-Ping Guo CAS Key Laboratory of Quantum Information University of Science and Technology of China(USTC)Hefei 230026China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of ChinaHefei 230026China Integrated Circuit Advanced Process Research and Development Center Institute of MicroelectronicsChinese Academy of Sciences(CAS)Beijing 100029China Hefei National Laboratory Hefei 230088China Beijing Superstring Academy of Memory Technology Beijing 100176China Origin Quantum Computing Company Limited Hefei 230026China

In semiconductor quantum dot systems,pulse distortion is a significant source of coherent errors,which impedes qubit characterization and ***,we demonstrate two calibration methods using a two-qubit system as the detector to correct distortion and calibrate the transfer function of the control *** methods are straightforward to implement,robust against noise,and applicable to a wide range of qubit *** two methods differ in correction accuracy and *** first,coarse predistortion(CPD)method,partially mitigates *** second,all predistortion(APD)method,measures the transfer function and significantly enhances exchange oscillation *** methods use exchange oscillation homogeneity as the metric and are suitable for any qubit driven by a diabatic *** believe these methods will enhance qubit characterization accuracy and operation quality in future applications.

关键词： quantum computation quantum dot pulse distortion

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Two-Stage Approach for Brain MR Image Synthesis: 2D Image Synthesis and 3D Refinement

arXiv

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arXiv 2024年

作者： Cho, Jihoon Park, Seunghyuck Park, Jinah School of Computing Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of

Despite significant advancements in automatic brain tumor segmentation methods, their performance is not guaranteed when certain MR sequences are missing. Addressing this issue, it is crucial to synthesize the missing MR images that reflect the unique characteristics of the absent modality with precise tumor representation. Typically, MRI synthesis methods generate partial images rather than full-sized volumes due to computational constraints. This limitation can lead to a lack of comprehensive 3D volumetric information and result in image artifacts during the merging process. In this paper, we propose a two-stage approach that first synthesizes MR images from 2D slices using a novel intensity encoding method and then refines the synthesized MRI. The proposed intensity encoding reduces artifacts when synthesizing MRI on a 2D slice basis. Then, the Refiner, which leverages complete 3D volume information, further improves the quality of the synthesized images and enhances their applicability to segmentation methods. Experimental results demonstrate that the intensity encoding effectively minimizes artifacts in the synthesized MRI and improves perceptual quality. Furthermore, using the Refiner on synthesized MRI significantly improves brain tumor segmentation results, highlighting the potential of our approach in practical applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Examining the Acceptance of a Secure and Systematic Personal Research Management Prototype for Higher Education Researchers 7

Examining the Acceptance of a Secure and Systematic Personal...

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7th International Conference on Internet Applications, Protocols, and Services, NETAPPS 2024

作者： Awang, Hapini Mansor, Nur Suhaili Adnan, Muhammad Hazim Shahatha Al-Mashhadani, Abdulrazak F. Ghazali, Osman Khamis, Shakiroh Institute for Advanced and Smart Digital Opportunities School of Computing Universiti Utara Malaysia Sintok Malaysia School of Computing Universiti Utara Malaysia Sintok Malaysia Faculty of Business Sohar University Sohar Oman Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Kampar Malaysia

ISBN: (纸本)9798331519513

Efficient management of research grants is crucial for academic and scientific progress. However, unsystematic research management always poses risks like failure to meet the deadline or even failure to complete the project. To address the challenges in this domain, a prototype called the Smart Personal Research Grant Management System (SPRGM) was developed. This prototype aims to support sustainable and efficient research grant management by providing accessible, high-quality tools tailored to the needs of researchers and administrators. To evaluate its acceptance among potential users, a survey was conducted involving researchers and grant administrators. The evaluation followed the technology Acceptance Model (TAM), examining Perceived Usefulness (PU), Perceived Ease of Use (PEOU), and Intention to Adopt (ITA) using descriptive statistics and correlation analysis. Results indicate the prototype achieved a high level of PU (mean=4.12), PEOU (mean=4.09), and ITA (mean=4.10). However, no significant correlation was found between PEOU, PU, and ITA, thus rejecting H1 (r = 0.726, p = 0.164), H2 (r = 0.321, p = 0.597), and H3 (r = 0.726, p = 0.164). The qualitative data was analyzed using sentiment and thematic analyses. Sentiment analysis revealed predominantly positive and neutral user perceptions, with an average sentiment score of +0.50 and a magnitude score of 0.51. Furthermore, the thematic analysis indicated that SPRGM is well-received by users, with some suggestions for improvement. This study extends the boundary of knowledge by exploring the role of digital technology in improving research grant management and promoting efficient use of resources in academic and research institutions. © 2024 IEEE.

关键词： Research and development management

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