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$\text{MR}^{2}$-Net: Retinal OCTA Image Stitching via Multi-Scale Representation Learning and Dynamic Location Guidance

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IEEE Journal of Biomedical and Health Informatics 2024年第1期29卷 482-494页

作者： Haiting Mao Yuhui Ma Dan Zhang Yanda Meng Shaodong Ma Yuchuan Qiao Huazhu Fu Caifeng Shan Da Chen Yitian Zhao Jiong Zhang Laboratory of Advanced Theranostic Materials and Technology Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo China School of Cyber Science and Engineering Ningbo University of Technology Ningbo China Department of Computer Science University of Exeter Exeter U.K. Institution of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Institute of High Performance Computing (IHPC) A*STAR Singapore School of Intelligence Science and Technology Nanjing University Nanjing China Qilu University of Technology Jinan China

Optical coherence tomography angiography (OCTA) plays a crucial role in quantifying and analyzing retinal vascular diseases. However, the limited field of view (FOV) inherent in most commercial OCTA imaging systems poses a significant challenge for clinicians, restricting the possibility to analyze larger retinal regions of high resolution. Automatic stitching of OCTA scans in adjacent regions may provide a promising solution to extend the region of interest. However, commonly-used stitching algorithms face difficulties in achieving effective alignment due to noise, artifacts and dense vasculature present in OCTA images. To address these challenges, we propose a novel retinal OCTA image stitching network, named $\text{MR}^{2}$ -Net, which integrates multi-scale representation learning and dynamic location guidance. In the first stage, an image registration network with a progressive multi-resolution feature fusion is proposed to derive deep semantic information effectively. Additionally, we introduce a dynamic guidance strategy to locate the foveal avascular zone (FAZ) and constrain registration errors in overlapping vascular regions. In the second stage, an image fusion network based on multiple mask constraints and adjacent image aggregation (AIA) strategies is developed to further eliminate the artifacts in the overlapping areas of stitched images, thereby achieving precise vessel alignment. To validate the effectiveness of our method, we conduct a series of experiments on two delicately constructed datasets, i.e., OPTOVUE-OCTA and SVision-OCTA. Experimental results demonstrate that our method outperforms other image stitching methods and effectively generates high-quality wide-field OCTA images, achieving a structural similarity index (SSIM) score of 0.8264 and 0.8014 on the two datasets, respectively.

关键词： Feature extraction Image stitching Retina Image registration Convolutional codes Bioinformatics Representation learning

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Energy-Efficient and Privacy-Preserved Incentive Mechanism for Federated Learning in Mobile Edge Computing

Energy-Efficient and Privacy-Preserved Incentive Mechanism f...

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IEEE International Conference on Communications (ICC)

作者： Jingyuan Liu Zheng Chang Geyong Min Yan Zhang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Information Technology University of Jyväskylä Jyväskylä Finland Department of Computer Science University of Exeter Exeter U.K. Department of Informatics University of Oslo Norway

In mobile edge computing (MEC)-assisted federated learning (FL), the MEC users can train data locally and send the results to the MEC server to update the global model. However, the implementation of FL may be prevented by the selfish nature of MEC users, as they need to contribute considerable data and computing resources while scarifying certain data privacy for the FL process. Therefore, it is of great importance to design an efficient incentive mechanism to motivate the users to join the FL. In this work, with explicit consideration of the impact of wireless transmission and data privacy, we design an energy-efficient and privacy-preserved incentive scheme to facilitate the FL process by investigating interactions between the MEC server and MEC users in a MEC-assisted FL system. Using a Stackelberg game model, we explore the transmit power allocation and privacy budget determination of MEC users and reward strategy of the MEC server, and then analyze the Stackelberg equilibrium. The simulation results demonstrate the effectiveness of our proposed scheme.

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Towards combating frequency simplicity-biased learning for domain generalization 24

Towards combating frequency simplicity-biased learning for d...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xilin He Jingyu Hu Qinliang Lin Cheng Luo Weicheng Xie Siyang Song Muhammad Haris Khan Linlin Shen Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University and Shenzhen Institute of Artificial Intelligence and Robotics for Society and Guangdong Provincial Key Laboratory of Intelligent Information Processing University of Exeter Mohamed bin Zayed University of Artificial Intelligence

ISBN: (纸本)9798331314385

Domain generalization methods aim to learn transferable knowledge from source domains that can generalize well to unseen target domains. Recent studies show that neural networks frequently suffer from a simplicity-biased learning behavior which leads to over-reliance on specific frequency sets, namely as frequency shortcuts, instead of semantic information, resulting in poor generalization performance. Despite previous data augmentation techniques successfully enhancing generalization performances, they intend to apply more frequency shortcuts, thereby causing hallucinations of generalization improvement. In this paper, we aim to prevent such learning behavior of applying frequency shortcuts from a data-driven perspective. Given the theoretical justification of models' biased learning behavior on different spatial frequency components, which is based on the dataset frequency properties, we argue that the learning behavior on various frequency components could be manipulated by changing the dataset statistical structure in the Fourier domain. Intuitively, as frequency shortcuts are hidden in the dominant and highly dependent frequencies of dataset structure, dynamically perturbating the over-reliance frequency components could prevent the application of frequency shortcuts. To this end, we propose two effective data augmentation modules designed to collaboratively and adaptively adjust the frequency characteristic of the dataset, aiming to dynamically influence the learning behavior of the model and ultimately serving as a strategy to mitigate shortcut learning. Code is available at https://***/C0notSilly/AdvFrequency.

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Split Federated Learning Empowered Vehicular Edge Intelligence: Adaptive Parellel Design and Future Directions

arXiv

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arXiv 2024年

作者： Qiang, Xianke Chang, Zheng Ye, Chaoxiong Hämäläinen, Timo Min, Geyong School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Faculty of Information Technology University of Jyväskylä P. O. Box 35 JyväskyläFIN-40014 Finland Department of Computer Science University of Exeter ExeterEX4 4QF United Kingdom

To realize ubiquitous intelligence of future vehicular networks, artificial intelligence (AI) is critical since it can mine knowledge from vehicular data to improve the quality of many AI driven vehicular services. By combining AI techniques with vehicular networks, Vehicular Edge Intelligence (VEI) can utilize the computing, storage, and communication resources of vehicles to train the AI models. Nevertheless, when executing the model training, the traditional centralized learning paradigm requires vehicles to upload their raw data to a central server, which results in significant communication overheads and the risk of privacy leakage. In this article, we first overview the system architectures, performance metrics and challenges ahead of VEI design. Then we propose to utilize distribute machine learning scheme, namely split federated learning (SFL), to boost the development of VEI. We present a novel adaptive and parellel SFL scheme and conduct corresponding analysis on its performance. Future research directions are highlighted to shed light on the efficient design of SFL. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital storage

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Decentralized Channel Management in WLANs with Graph Neural Networks

Decentralized Channel Management in WLANs with Graph Neural ...

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IEEE International Conference on Communications (ICC)

作者： Zhan Gao Yulin Shao Deniz Gündüz Amanda Prorok Department of Computer Science and Technology University of Cambridge Cambridge UK Department of Electrical and Electronic Engineering Imperial College London London UK Department of Engineering University of Exeter Exeter UK

Wireless local area networks (WLANs) manage multiple access points (APs) and assign scarce radio frequency resources to APs for satisfying traffic demands of associated user devices. This paper considers the channel allocation problem in WLANs that minimizes the mutual interference among APs, and puts forth a learning-based solution that can be implemented in a decentralized manner. We formulate the channel allocation problem as an unsupervised learning problem, parameterize the control policy of radio channels with graph neural networks (GNNs), and train GNNs with the policy gradient method in a model-free manner. The proposed approach allows for a decentralized implementation due to the distributed nature of GNNs and is equivariant to network permutations. The former provides an efficient and scalable solution for large network scenarios, and the latter renders our algorithm independent of the AP reordering. Empirical results are presented to evaluate the proposed approach and corroborate theoretical findings.

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Clinical Insight-Augmented Multi-View Learning for Alzheimer’s Detection in Retinal OCTA Images

Clinical Insight-Augmented Multi-View Learning for Alzheimer...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yuandi Zhang Jinkui Hao Botian Zheng Yonghuai Liu Yanda Meng Jiong Zhang Yang Chen Chunfeng Yang Yitian Zhao School of Computer Science and Engineering Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University)-Ministry of EducationSoutheast University Nanjing China Laboratory of Advanced Theranostic Materials and Technology Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo China Department of Computer Science Edge Hill University Ormskirk UK Department of Computer Science University of Exeter Exeter UK

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Alzheimer’s disease (AD) poses a significant global challenge, with a notable absence of accessible and cost-effective diagnostic tools for widespread AD detection. The retina, mirroring the brain in anatomy and physiology, has emerged as a potential avenue for rapid AD identification through retinal imaging. The current retinal image-based AD detection methods usually focus primarily on the macular area, but ignore the potential value that the optic disc region may have for the detection task. In this study, we leverage both macular- and disc-centered OCTA images and propose a multi-region fusion framework for AD detection. Based on clinical evidence, we integrate handcrafted features into the framework to improve model performance and interpretability. Specifically, vascular morphological parameters extracted from the macular and disc regions are used as input to a revalued KNN model to improve predictive capabilities. Furthermore, recognizing the significance of extracting and utilizing complementary information from the macular and optic disc regions, we propose an uncertainty-guided strategy based on Dempster-Shefer Theory (DST) to fuse knowledge from different regions. This approach considers each region’s forecast quality and significantly improves the effectiveness and robustness of the model. Through comparative analysis with existing methods, we have demonstrated that our method outperforms the state-of-the-art ones and provides more valuable pathological evidence for the association between retinal vascular changes and AD.

关键词： Accuracy Uncertainty Manuals Predictive models Feature extraction Retina Optical imaging Robustness Physiology Alzheimer's disease

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Probabilistic-Search and Neighbor-Density Based Doppler-Shift Acquisition in Space Communications

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IEEE Transactions on Communications 2025年

作者： Zhang, Zhaowei Du, Shuai Liu, Lin Liu, Hui Zuo, Jiakuo Miao, Wang Zhao, Haitao Nanjing University of Posts and Telecommunications School of Internet of Things China Nanjing University State Key Laboratory for Novel Software Technology China Jinling Institute of Technology School of Software Engineering China University of Exeter Department of Computer Science United Kingdom

In space communications, the signal's long-distance transmission between a flying transmitter-receiver pair encounters two obstacles: a huge path-loss and a high-speed movement. The huge path-loss results in a low signal-to-noise ratio (SNR) and the high-speed movement brings a dynamic Doppler-shift, thus posing a great challenge for Doppler-shift acquisition. Under the low SNR, the Doppler-shift acquisition has to accumulate many symbols in a long-time period. However, during this period, the dynamic Doppler-shift disperses all these symbols' total energy over a wide range, thus causing the energy dispersion problem. To address this problem, we propose a probabilistic-search and neighbor-density (PSND) scheme, where the probabilistic-search considers the element's amplitude and sacrifices some redundant signal elements for a narrower search-range, while the neighbordensity sums the signal element's all neighbors for a larger signal energy. The PSND scheme includes two algorithms: the Generic PSND and Iterative PSND. The Generic PSND algorithm first selects some search-elements with their probabilities to construct a probabilistic-search-range, then selects some density-elements with their neighbor-elements to derive the neighbor-densities, and finally searches the largest neighbor-density to obtain the acquisition result. Built upon the Generic PSND algorithm above, the Iterative PSND algorithm iteratively updates the searchelements to further strengthen the neighbor-density in a narrower probabilistic-search-range. Moreover, the simulations results have demonstrated our PSND scheme's higher acquisition probability, as compared with the existing schemes. © 1972-2012 IEEE.

关键词： Signal to noise ratio

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A Robust Finite Time Longitudinal Speed Control for a Catenary-based Electric Vehicle

A Robust Finite Time Longitudinal Speed Control for a Catena...

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IEEE Conference on Decision and Control

作者： Gianmario Rinaldi Antonella Ferrara Prathyush P Menon Department of Engineering Faculty of Environment Science and Economy University of Exeter United Kingdom Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

The electrification of the transportation sector plays a crucial role in reducing the global carbon footprint. Among the various solutions, catenary-based electric vehicles have attracted a great deal of interest from researchers and practitioners. This article considers a vehicle powered by a battery energy storage system (BESS) and a catenary system and proposes a robust finite-time longitudinal speed control strategy. Drawing inspiration from the Second-Order Sliding Mode (2-SOSM) methodology, the approach ensures that the control input remains within the limits set by the maximum torque of the motor. The strategy demonstrates robustness against uncertainties and disturbances, eliminating the need to know the model parameters. The simulation case study considers the effects of real road data, including slope variations, on power management. A comparative analysis with the conventional Proportional Integral (PI) method highlights the superior performance of the proposed strategy.

关键词： Uncertainty Torque Roads Power system management Velocity control Transportation Electric vehicles Motors Robustness Regulation

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Performance Analysis of IEEE 802.11p for the Internet of Vehicles with Bursty Packet Errors

Performance Analysis of IEEE 802.11p for the Internet of Veh...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Ning Wang Jia Hu Department of Computer Science College of Engineering Mathematics and Physical Sciences University of Exeter Exeter UK

The Quality of Service (QoS) requirements of vehicular services have become more and more stringent in intelligent connected vehicles. Hence, the Enhanced Distributed Channel Access (EDCA) mechanism has been utilized in IEEE 802.11p to achieve multi-class QoS for the Internet of Vehicles. Recently, some analytical models have been developed to evaluate the performance of the IEEE 802.11p. Nevertheless, most works are under the assumption of ideal wireless channels and infinite buffer sizes that are not realistic for vehicular networks’ operating environments. In this paper, we propose an analytical model for IEEE 802.11p with bursty error transmission based on the Queuing Theory. Especially, the transmission queue is modelled as an M/G/1/N queueing system. The accuracy of this model has been validated by a set of simulation experiments based on ns-3 and SUMO. The model can be used as a cost-effective tool to investigate the performance of 802.11p under various network conditions.

关键词： Wireless communication Analytical models Privacy Connected vehicles Computational modeling Quality of service Performance analysis

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An Optimized Federation Model for Park-Level Integrated Energy Systems in Industrial Internet of Things

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IEEE Internet of Things Journal 2024年

作者： Bai, Xingzhen Yang, Junxiang Yuan, Xiyao Zhou, Weiwei Liu, Lu Howlader, S M Ruhul Kabir Shandong University of Science and Echnology School of Electrical and Automation Engineering Qingdao266590 China State Grid Intelligence Technology Co. Ltd Jinan China Shandong Yingcai University School of Business Jinan250104 China University of Exeter Department of Computer Science ExeterEX4 4RN United Kingdom University of Leicester School of Computing and Mathematical Sciences Leicester United Kingdom

In order to achieve energy consumption optimization and decarbonization in the Industrial Internet of Things, this paper establishes a novel framework for optimal scheduling of park-level integrated energy systems. This framework incorporates carbon capture technologies alongside a multi-energy joint supply subsystem model featuring hydrogen storage, complemented by a structured stepped carbon trading mechanism. Furthermore, a novel optimized federation model is developed to balance low carbon emissions and economic performance by federating the carbon capture system into the tiered carbon trading mechanism. The primary objective of this model in the context of the Industrial Internet of Things is to minimize energy procurement costs, wind abandonment costs, and carbon trading expenses while maximizing revenues from carbon dioxide sales. The CPLEX optimization tool is utilized to address this complex challenge. Finally, several scenarios are simulated to demonstrate the effectiveness of the optimized park-level integrated energy systems in reducing the operational costs and minimizing carbon footprint. © 2014 IEEE.

关键词： Carbon capture and utilization

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