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1,115 results on '"Wang, Guanjun"'

1. Federated Distillation for Medical Image Classification: Towards Trustworthy Computer-Aided Diagnosis

Author

Ren, Sufen, Hu, Yule, Chen, Shengchao, and Wang, Guanjun

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Medical image classification plays a crucial role in computer-aided clinical diagnosis. While deep learning techniques have significantly enhanced efficiency and reduced costs, the privacy-sensitive nature of medical imaging data complicates centralized storage and model training. Furthermore, low-resource healthcare organizations face challenges related to communication overhead and efficiency due to increasing data and model scales. This paper proposes a novel privacy-preserving medical image classification framework based on federated learning to address these issues, named FedMIC. The framework enables healthcare organizations to learn from both global and local knowledge, enhancing local representation of private data despite statistical heterogeneity. It provides customized models for organizations with diverse data distributions while minimizing communication overhead and improving efficiency without compromising performance. Our FedMIC enhances robustness and practical applicability under resource-constrained conditions. We demonstrate FedMIC's effectiveness using four public medical image datasets for classical medical image classification tasks., Comment: Work in progress. This paper is the first to introduce intra-client knowledge distillation in the context of trustworthy medical image classification. arXiv admin note: text overlap with arXiv:2401.01493

Published
2024

3. Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia from Chest X-Ray Images

Author

Chen, Shengchao, Ren, Sufen, Wang, Guanjun, Huang, Mengxing, and Xue, Chenyang

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Chest imaging plays an essential role in diagnosing and predicting patients with COVID-19 with evidence of worsening respiratory status. Many deep learning-based approaches for pneumonia recognition have been developed to enable computer-aided diagnosis. However, the long training and inference time makes them inflexible, and the lack of interpretability reduces their credibility in clinical medical practice. This paper aims to develop a pneumonia recognition framework with interpretability, which can understand the complex relationship between lung features and related diseases in chest X-ray (CXR) images to provide high-speed analytics support for medical practice. To reduce the computational complexity to accelerate the recognition process, a novel multi-level self-attention mechanism within Transformer has been proposed to accelerate convergence and emphasize the task-related feature regions. Moreover, a practical CXR image data augmentation has been adopted to address the scarcity of medical image data problems to boost the model's performance. The effectiveness of the proposed method has been demonstrated on the classic COVID-19 recognition task using the widespread pneumonia CXR image dataset. In addition, abundant ablation experiments validate the effectiveness and necessity of all of the components of the proposed method., Comment: Accepted by the IEEE Journal of Biomedical and Health Informatic, doi: 10.1109/JBHI.2023.3247949

Published
2022
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18. Rapid Delivery of Wartime Materials in Plateau Alpine Border Area Based on SRLV

Author

Ma, Zhenwei, Wang, Guanjun, Chen, Hongbo, Wang, Jinbo, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Yan, Liang, editor, and Deng, Yimin, editor

Published
2023
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22. Research on detection of parking space based on the combination of magnetoresistance and microwave radar sensor under IoT platform

Author

Wang Guanjun and Wu Jing

Subjects
Mathematics, QA1-939
Abstract

An intelligent system is the most effective measure to improve transportation efficiency and urban congestion. The traditional parking space detection technology has some defects, such as low detection accuracy, inconvenient wiring and high cost, which are not conducive to the application of an intelligent detection system. In view of the widespread imbalance between demand and supply of parking spaces, the detection technology based on magnetoresistance sensors has obvious advantages in network, cost, accuracy and operating environment. Therefore, in this paper, a parking space detection system is studied and designed based on the combination of magnetoresistance and microwave radar sensor under the IoT platform, which aims at improving the utilisation rate of parking spaces, meeting the parking space supply, reducing the degree of urban congestion and advancing the management level and efficiency. The effectiveness and practicability of the designed system are verified through data analysis and various functional tests.

Published
2023
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33. Lightweight Miniaturized Terminal Based on High Precision GNSS Module

Author

Wang, Guanjun, Zhu, Liang, Lu, Chao, Xie, Yayun, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Yang, Changfeng, editor, and Xie, Jun, editor

Published
2022
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37. Optimal resource allocation in common bus performance sharing systems with transmission loss.

Author

Gu, Liudong, Wang, Guanjun, and Zhou, Yifan

Subjects
*RELIABILITY in engineering, *RESOURCE allocation, *GENETIC algorithms, *BUSES, *SHARING
Abstract

The reliability modeling and optimization of performance sharing systems (PSSs) are of vital importance due to their wide applications. Existing research mainly focuses on evaluating and maximizing the reliability of PSSs. However, in many practical systems, decision‐makers tend to prioritize the average cost of the system over its reliability. This paper studies the resource allocation optimization in common bus PSSs. In such systems, each unit has binary‐state random performance to satisfy the multi‐state random demand. The surplus performance can be shared via a common bus with transmission loss between the common bus and the unit. The performance allocation, performance transmission, and unsupplied demand incur costs. Resource allocation strategies are determined by optimization models considering different objective functions and constraints. Additionally, transmission loss between the common bus and the unit is considered. A genetic algorithm is employed to efficiently find the optimal allocation strategies. Numerical examples prove the effectiveness of the proposed models in improving system reliability and reducing system costs. [ABSTRACT FROM AUTHOR]

Published
2024
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