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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care system for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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Reliable Task Offloading for Vehicular Fog Computing under Information Asymmetry and Information Uncertainty

Reliable Task Offloading for Vehicular Fog Computing under I...

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作者： Zhou, Zhenyu Liao, Haijun Zhao, Xiongwen Ai, Bo Guizani, Mohsen State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Electrical and Electronic Engineering North China Electric Power University Beijing China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Department of Computer Science and Engineering Qatar University Doha Qatar

Vehicular fog computing has emerged as a cost-efficient solution for task processing in vehicular networks. However, how to realize effective server recruitment and reliable task offloading under information asymmetry and uncertainty remains a critical challenge. In this paper, we adopt a two-stage task offloading framework to address this challenge. First, we propose a convex-concave-procedure-based contract optimization algorithm for server recruitment, which aims to maximize the expected utility of the operator with asymmetric information. Then, a low-complexity and stable task offloading mechanism is proposed to minimize the total network delay based on the pricing-based matching. Furthermore, we extend the work to the scenario of information uncertainty and develop a matching-learning-based task offloading mechanism, which takes both occurrence awareness and conflict awareness into consideration. Simulation results demonstrate that the proposed algorithm can effectively motivate resource sharing and guarantee bounded deviation from the optimal performance without the global information. © 2019 IEEE.

关键词： Fog computing

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Hierarchical Clustering of Complex Symbolic Data and Application for Emitter Identification

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Journal of computer Science & Technology 2018年第4期33卷 807-822页

作者： Xin Xu Jiaheng Lu Wei Wang Laboratory of Science and Technology on Information System Engineering Nanjing Research Institute of Electronics Engineering Nanjing 21000~ China Department of Computer Science University of Helsinki Helsinki 00014 Finland State Key Laboratory for Novel Software and Technology Nanjing University Nanjing 210046 China

It is well-known that the values of symbolic variables may take various forms such as an interval, a set of stochastic measurements of some underlying patterns or qualitative multi-values and so on. However, the majority of existing work in symbolic data analysis still focuses on interval values. Although some pioneering work in stochastic pattern based symbolic data and mixture of symbolic variables has been explored, it still lacks flexibility and computation efficiency to make full use of the distinctive individual symbolic variables. Therefore, we bring forward a novel hierarchical clustering method with weighted general Jaccard distance and effective global pruning strategy for complex symbolic data and apply it to emitter identification. Extensive experiments indicate that our method has outperformed its peers in both computational efficiency and emitter identification accuracy.

关键词： symbolic data analysis stochastic pattern fuzzy interval hierarchical clustering emitter identification

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DPNet: Dynamic Pooling Network for Tiny Object Detection

arXiv

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arXiv 2025年

作者： Gong, Luqi Chen, Haotian Chen, Yikun Yao, Tianliang Li, Chao Zhao, Shuai Han, Guangjie School of Computer Science Beijing University of Posts and Telecommunications Beijing China Research Center for Space Computing System Zhejiang Lab Hangzhou China SWJTU-LEEDS Joint School Southwest Jiaotong University Chengdu China Guangdong Zhiyun City construction Technology Co. LTD Zhuhai China Department of Control Science and Engineering College of Electronic and Information Engineering Tongji University Shanghai China Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education Hohai University China

In unmanned aerial systems, especially in complex environments, accurately detecting tiny objects is crucial. Resizing images is a common strategy to improve detection accuracy, particularly for small objects. However, simply enlarging images significantly increases computational costs and the number of negative samples, severely degrading detection performance and limiting its applicability. This paper proposes a Dynamic Pooling Network (DPNet) for tiny object detection to mitigate these issues. DPNet employs a flexible down-sampling strategy by introducing a factor (df) to relax the fixed downsampling process of the feature map to an adjustable one. Furthermore, we design a lightweight predictor to predict df for each input image, which will be used to decrease the resolution of feature map in backbone. Thus, we achieve input-aware downsampling. We design an Adaptive Normalization Module (ANM) to make a unified detector well compatible with different dfs. At the same time, we also design a guidance loss to supervise the predictor’s training. DPNet realizes the dynamic allocation of computing resources to trade off detection accuracy and efficiency through this. Experiments on the TinyCOCO and TinyPerson datasets show that our DPNet can save over 35% and 25% GFLOPs, respectively, while maintaining comparable detection *** code will be made publicly available. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

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DPNet: Dynamic Pooling Network for Accurate and Efficient Size-Aware Tiny Object Detection

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IEEE Internet of Things Journal 2025年

作者： Gong, Luqi Chen, Haotian Chen, Yikun Yao, Tianliang Li, Chao Zhao, Shuai Han, Guangjie Beijing University of Posts and Telecommunications School of Computer Science Beijing China Zhejiang Lab Research Center for Space Computing System Hangzhou China Southwest Jiaotong University SWJTU-LEEDS Joint School Chengdu China LTD Guangdong Zhiyun City construction Technology Co Zhuhai China Tongji University College of Electronic and Information Engineering Department of Control Science and Engineering Shanghai China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

In unmanned aerial systems, especially in complex environments, accurately detecting tiny objects is crucial. Resizing images is a common strategy to improve detection accuracy, particularly for small objects. However, simply enlarging images significantly increases computational costs and the number of negative samples, severely degrading detection performance and limiting its applicability. This paper proposes a Dynamic Pooling Network (DPNet) for tiny object detection to mitigate these issues. DPNet employs a flexible down-sampling strategy by introducing a factor (df) to relax the fixed down-sampling process of the feature map to an adjustable one. Furthermore, we design a lightweight predictor to predict df for each input image, which will be used to decrease the resolution of feature map in backbone. Thus, we achieve input-aware down-sampling. We design an Adaptive Normalization Module (ANM) to make a unified detector well compatible with different dfs. At the same time, we also design a guidance loss to supervise the predictor’s training. DPNet realizes the dynamic allocation of computing resources to trade off detection accuracy and efficiency through this. Experiments on the TinyCOCO and TinyPerson datasets show that our DPNet can save over 35% and 25% GFLOPs, respectively, while maintaining comparable detection *** code will be made publicly available. © 2014 IEEE.

关键词： Object detection

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Unbalanced incomplete multi-view clustering via the scheme of view evolution: Weak views are meat;strong views do eat

arXiv

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arXiv 2020年

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Oliver The School of Computer Science and Technology Key Laboratory of Information Storage System Ministry of Education of China Huazhong University of Science and Technology Wuhan430074 China The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China The Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Incomplete multi-view clustering is an important technique to deal with real-world incomplete multi-view data. Previous methods assume that all views have the same incompleteness, i.e., balanced incompleteness. However, different views often have distinct incompleteness, i.e., unbalanced incompleteness, which results in strong views (low-incompleteness views) and weak views (high-incompleteness views). The unbalanced incompleteness prevents us from directly using previous methods. In this paper, inspired by the effective biological evolution theory, we design the novel scheme of view evolution to cluster strong and weak views. Moreover, we propose an Unbalanced Incomplete Multi-view Clustering method (UIMC), which is the first effective method based on view evolution for unbalanced incomplete multi-view clustering. Compared with previous methods, UIMC has two unique advantages: 1) it proposes weighted multi-view subspace clustering to integrate unbalanced incomplete views, which effectively solves the unbalanced incomplete multi-view clustering problem;2) it designs the low-rank representation to recover the data, which diminishes the impact of the incompleteness and noises. Extensive experimental results demonstrate that UIMC improves the clustering performance by up to 40% on three evaluation metrics over other state-of-the-art methods. We provide codes for all of our experiments in https://***/ZeusDavide/TETCI_UIMC. Copyright © 2020, The Authors. All rights reserved.

关键词： Clustering algorithms

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A Malware Identification and Detection Method Using Mixture Correntropy-Based Deep Neural Network 3rd

A Malware Identification and Detection Method Using Mixture ...

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3rd International Conference on Cyberspace Data and Intelligence, Cyber DI 2019, and the International Conference on Cyber-Living, Cyber-Syndrome, and Cyber-Health, CyberLife 2019

作者： Luo, Xiong Li, Jianyuan Wang, Weiping Gao, Yang Zhao, Wenbing School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China Beijing Intelligent Logistics System Collaborative Innovation Center Beijing101149 China Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15261 United States China Information Technology Security Evaluation Center Beijing100085 China Department of Electrical Engineering and Computer Science Cleveland State University ClevelandOH44115 United States

ISBN: (纸本)9789811519215

With the rapid development of CPS technology, the identification and detection of malware has become a matter of concern in the industrial application of CPS. Currently, advanced machine learning methods such as deep learning are popular in the research of malware identification and detection, and some progress has been made so far. However, there are also some problems. For example, considering the existing noise or outliers in the datasets of malware, some methods are not robust enough. Therefore, the accuracy of classification of malware still needs to be improved. Aiming at it, we propose a novel method thought the combination of correntropy and deep neural network (DNN). In our proposed method for malware identification and detection, given the success of mixture correntropy as an effective similarity measure in addressing complex dataset with noise, it is therefore incorporated into a popular DNN, i.e., convolutional neural network (CNN), to reconstruct its loss function, with the purpose of further detecting the features of outliers. We present the detailed design process of our proposed method. Furthermore, the proposed method is tested both on a popular benchmark dataset and a real-world malware classification dataset, to verify its learning performance. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Mixtures

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Smart radio environments

arXiv

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arXiv 2021年

作者： Gradoni, Gabriele Renzo, Marco Di Díaz-Rubio, Ana Tretyakov, Sergei A. Caloz, Christophe Gradoni, Gabriele Peng, Zhen Alu, Andrea Lerosey, Geoffroy Fink, Mathias Galdi, Vincenzo Cui, Tie Jun Frazier, Benjamin W. Anlage, Steven M. Salucci, Marco Massa, Andrea Cheng, Qiang Wang, Jinghe Jin, Shi Dardari, Davide Decarli, Nicoló Yurduseven, Okan Matthaiou, Michail Kenney, Mitchell Gordon, George Georgiou, Orestis Nguyen, Cam Ly Martini, Enrica Maci, Stefano Wakatsuchi, Hiroki Phang, Sendy School of Mathematical Sciences Department of Electrical and Electronic Engineering University of Nottingham Nottingham United Kingdom Laboratoire des Signaux et Systémes CNRS CentraleSupélec Université Paris-Saclay Gif-sur-Yvette Paris France Department of of Electronics and Nanoengineering Aalto University Espoo Finland META Research Group KU Leuven Belgium School of Mathematical Sciences Department of Electrical and Electronic Engineering University of Nottingham University Park NG72RD United Kingdom Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign IL United States Advanced Science Research Center City University of New York United States Greenerwave 6 rue Jean Calvin Paris75005 France Institut Langevin 1 rue Jussieu Paris75005 France Fields & Waves Lab Department of Engineering University of Sannio Benevento Italy State Key Laboratory of Millimeter Waves Southeast University Nanjing China Applied Physics Laboratory Johns Hopkins University LaurelMD20723 United States Department of Electrical and Computer Engineering Department of Physics Quantum Materials Center University of Maryland College ParkMD20742 United States Trento Italy Chengdu China Beijing China Institute of Electromagnetic Space State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China National Mobile Communications Research Laboratory Southeast University Nanjing China University of Bologna Bologna Italy Bologna Italy Centre for Wireless Innovation Institute of Electronics Communications and Information Technology Queen’s University Belfast Belfast United Kingdom Department of Electrical and Electronic Engineering University of Nottingham Nottingham United Kingdom Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Wireless System Laboratory Corporate Research & Development Center Toshiba Corporation Kawasaki Japan Department of Information Engineering an

This Roadmap takes the reader on a journey through the research in electromagnetic wave propagation control via reconfigurable intelligent surfaces. Meta-surface modelling and design methods are reviewed along with physical realisation techniques. Several wireless applications are discussed, including beam-forming, focusing, imaging, localisation, and sensing, some rooted in novel architectures for future mobile communications networks towards 6G. © 2021, CC BY.

关键词： Electromagnetic wave propagation

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OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines

arXiv

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arXiv 2022年

作者： Babier, Aaron Mahmood, Rafid Zhang, Binghao Alves, Victor G.L. Barragán-Montero, Ana Maria Beaudry, Joel Cardenas, Carlos E. Chang, Yankui Chen, Zijie Chun, Jaehee Diaz, Kelly Eraso, Harold David Faustmann, Erik Gaj, Sibaji Gay, Skylar Gronberg, Mary Guo, Bingqi He, Junjun Heilemann, Gerd Hira, Sanchit Huang, Yuliang Ji, Fuxin Jiang, Dashan Giraldo, Jean Carlo Jimenez Lee, Hoyeon Lian, Jun Liu, Shuolin Liu, Keng-Chi Marrugo, José Miki, Kentaro Nakamura, Kunio Netherton, Tucker Nguyen, Dan Nourzadeh, Hamidreza Osman, Alexander F.I. Peng, Zhao Muñoz, José Darío Quinto Ramsl, Christian Rhee, Dong Joo Rodriguez, Juan David Shan, Hongming Siebers, Jeffrey V. Soomro, Mumtaz H. Sun, Kay Hoyos, Andrés Usuga Valderrama, Carlos Verbeek, Rob Wang, Enpei Willems, Siri Wu, Qi Xu, Xuanang Yang, Sen Yuan, Lulin Zhu, Simeng Zimmermann, Lukas Moore, Kevin L. Purdie, Thomas G. McNiven, Andrea L. Chan, Timothy C.Y. Department Of Mechanical And Industrial Engineering University Of Toronto TorontoON Canada Vector Institute TorontoON Canada Department Of Radiation Oncology University Of Virginia Health System CharlottesvilleVA United States Department Of Molecular Imaging Radiation Oncology UCLouvain Louvain-la-Neuve Belgium Department Of Radiation Oncology Memorial Sloan Kettering Cancer Center New YorkNY United States Department Of Radiation Oncology The University Of Alabama At Birmingham BirminghamAL United States Department Of Engineering And Applied Physics University Of Science And Technology Of China Hefei China Shenying Medical Technology Co. Ltd. Guangdong Shenzhen China Department Of Radiation Oncology Yonsei University Seoul Korea Republic of Department Of Physics National University Of Colombia Medellín Colombia Atominstitut Vienna University Of Technology Vienna Austria Department Of Biomedical Engineering Cleveland Clinic ClevelandOH United States Department Of Radiation Physics The University Of Texas MD Anderson Cancer Center HoustonTX United States Department Of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Department Of Radiation Oncology Medical University Of Vienna Vienna Austria Department Of Biomedical Engineering Johns Hopkins University BaltimoreMD United States Department Of Radiation Oncology Peking University Cancer Hospital And Institute Beijing China Department Of Electrical Engineering And Automation Anhui University Hefei China Department Of Radiation Oncology Massachusetts General Hospital BostonMA United States Department Of Radiation Oncology University Of North Carolina At Chapel Hill Chapel HillNC United States Department Of Medical Imaging Taiwan AI Labs Taipei Taiwan Department Of Biomedical And Health Sciences Hiroshima University Hiroshima Japan Laboratory Department Of Radiation Oncology The University Of Texas Southwestern Medical Center DallasTX United States Department Of Radiatio

We establish an open framework for developing plan optimization models for knowledge-based planning (KBP) in radiotherapy. Our framework includes reference plans for 100 patients with head-and-neck cancer and high-quality dose predictions from 19 KBP models that were developed by different research groups during the OpenKBP Grand Challenge. The dose predictions were input to four optimization models to form 76 unique KBP pipelines that generated 7600 plans. The predictions and plans were compared to the reference plans via: dose score, which is the average mean absolute voxel-by-voxel difference in dose a model achieved;the deviation in dosevolume histogram (DVH) criterion;and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models. The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50 to 0.62, which indicates that the quality of the predictions is generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBPgenerated plans performed significantly better (P © 2022, CC BY.

关键词： Forecasting

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3D printing of an advanced design emergency mechanical ventilator, in response to the COVID 19 pandemic

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AIP Conference Proceedings 2022年第1期2437卷

作者： K. Kalkanis P. Papageorgas S. D. Kaminaris D. Piromalis G. Banis A. Batagiannis D. Mpelesis 1School of Engineering. Department of Electrical and Electronics Engineering University of West Attica Campus 2 Thivon 250 Aigaleo Attica Greece 2School of Engineering. Department of Industrial Design and Production Engineering University of West Attica Campus 2 Thivon 250 Aigaleo Attica Greece 33-Ψ Digital Engineering SMPC.Georgiou Griva Digeni 50 Larnaca Cyprus 4Laboratory of Electronic Sensors School of Electrical and Computer Engineering National TU of Athens Zografou Campus 15780 Athens Greece

Additive manufacturing is a rapidly evolving technological field, beyond he simple application of 3D printers, leading to an entirely new means of designing and manufacturing objects, paving the way for novel and unexplored possibilities. It is currently used in the production of mechanical components and tooling, medical and surgical implants and, also, to facilitate surgical design. The advantages stemming from this technology should be facilitated to combat the COVID-19 pandemic. With cases of respiratory failure and respiratory distress globally soaring and unavailability of standard automated ventilators in certain regions, the need to meet this demand is crucial. In this context, the research team considered it appropriate to move towards finding a solution to the problem of shortage in respirators in health systems worldwide. Having originally received plans for 3D printing from the University of Illinois, which produced disposable mechanical respirators with this technology, the possibility of optimizing and producing them at UNIWA was explored. The CAD files were analyzed in order to evaluate their printability resulting in partial redesign in order to limit dimensional variability, acceptable surface finish and make the printing process generally easier, faster, and less expensive. In addition, these modifications make the parts more suitable for casting, if high output is required. Metallic and thermoplastic composite respirators were produced and then tested in real operating conditions, i.e. with oxygen supply resembling a hospital environment. The initial results are considered positive, both in the quality of construction and in their performance. Furthermore, combination of manufacturing technologies is assessed, aiming at high output production.

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