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Cross-Image Siamese Graph Convolutional Network For Fine-Grained Image Retrieval in Diabetic Retinopathy

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SSRN

SSRN 2023年

作者： Chen, Fang Zhao, Weiling Zhou, Xiaobo School of Electronics and Information Engineering Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX77030 United States Center for Computational Systems Medicine

The categories of diabetic retinopathy (DR) are interrelated, and different ophthalmologists often give different results for the same fundus image. Automatic cross image retrieval of DR can provide an effective diagnostic solution for ophthalmologists and is of great significance in clinical practice. Cross-image (i.e. left and right fundus images for a patient) information is highly correlated and complementary and can be harnessed to improve various computer vision tasks such as image classification, object detection, image segmentation, and image retrieval. Previous studies did not explore the correlation between lesion areas in left and right fundus images of patients, limiting the effective diagnosis of DR. In this study, we proposed a cross-image siamese graph convolutional network(CIS-GCN) to retrieve fine-grained diabetic retinopathy fundus images. First, we constructed a global-specific structure to obtain the specific features of the left and right eyes. Then, we passed the specific features through the pathological localization network to obtain the location features of the lesion. Finally, a graph convolutional neural network was introduced to construct node sets for the left and right eyes, respectively, to represent relatively consistent regions in the fundus images of patients and learn their correlations. We tested our method using Diabetic Retinopathy Detection datasets and the results showed that our algorithm outperforms other state-of-the-art methods by 2.2% ~ 3.7% in image data retrieval. © 2023, The Authors. All rights reserved.

关键词： Object detection

Risk Contagion in Interbank Lending Networks: A Multi-Agent-Based Modeling and Simulation Perspective

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SSRN

SSRN 2024年

作者： Ding, Zhijun Yan, Huanlan Chen, Yijun The Key Laboratory of Embedded System and Service Computing Tongji University No.4800 Caoan Gonglu Road Shanghai201804 China The Department of Computer Science and Technology Tongji University No.4800 Caoan Gonglu Road Shanghai201804 China Shanghai Artificial Intelligence Laboratory No.701 Yunjin Road Shanghai200232 China

The interbank lending network, while facilitating transactions, poses risk contagion in the banking system. To address challenges in existing research, this paper models the interbank lending network using a multi-agent approach and generates a dynamic network through a bidirectional matching mechanism(BiMM). Additionally, We divide banks into three categories based on realistic bank balance sheet data. Given the fact that banks of different categories exhibit different degrees of contagion, we propose the weighted DebtRank (W-DebtRank). Then, comprehensive simulation analysis is conducted on the risk contagion of heterogeneous banks in the dynamic network using W-DebtRank. The proposed method addresses four issues: the heterogeneity of banks, the dynamic nature of networks, the practicality of lending strategies, and the comprehensiveness of risk analysis. The model is validated using the SRISK metric and the Monte Carlo-based SYMBOL simulation model. Through multiple perspectives in simulation experiments, the risk contagion process among banks is deeply investigated in our paper. © 2024, The Authors. All rights reserved.

关键词： Computational methods

Modeling and analysis of three properties of mobile interactive systems based on variable Petri nets

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arXiv 2021年

作者： Yang, Ru Ding, Zhijun Jiang, Changjun Zhou, MengChu The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University The Department of Computer Science and Technology Tongji University Shanghai201804 China The Institute of Systems Engineering Macau University of Science and Technology 999078 China The Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Due to the mobility and frequent disconnections, the correctness of mobile interaction systems, such as mobile robot systems and mobile payment systems, are often difficult to analyze. This paper introduces three critical properties of systems, called system connectivity, interaction soundness and data validity, and presents a related modeling and analysis method, based on a kind of Petri nets called VPN. For a given system, a model including component nets and interaction structure nets is constructed by using VPNs. The component net describes the internal process of each component, while the interaction structure net reflects the dynamic interaction between components. Based on this model, three properties are defined and analyzed. The case study of a practical mobile payment system shows the effectiveness of the proposed method. Copyright © 2021, The Authors. All rights reserved.

关键词： Petri nets

Two-stream Hierarchical Similarity Reasoning for Image-text Matching

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arXiv 2022年

作者： Chen, Ran Wang, Hanli Wang, Lei Kwong, Sam The Department of Computer Science & Technology Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China DeepBlue Academy of Sciences Shanghai200336 China The Department of Computer Science City University of Hong Kong Hong Kong

Reasoning-based approaches have demonstrated their powerful ability for the task of image-text matching. In this work, two issues are addressed for image-text matching. First, for reasoning processing, conventional approaches have no ability to find and use multi-level hierarchical similarity information. To solve this problem, a hierarchical similarity reasoning module is proposed to automatically extract context information, which is then co-exploited with local interaction information for efficient reasoning. Second, previous approaches only consider learning single-stream similarity alignment (i.e., image-to-text level or text-to-image level), which is inadequate to fully use similarity information for image-text matching. To address this issue, a two-stream architecture is developed to decompose image-text matching into image-to-text level and text-to-image level similarity computation. These two issues are investigated by a unifying framework that is trained in an end-to-end manner, namely two-stream hierarchical similarity reasoning network. The extensive experiments performed on the two benchmark datasets of MSCOCO and Flickr30K show the superiority of the proposed approach as compared to existing state-of-the-art methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolutional neural networks

Classification of facial diagnosis gloss in Chinese medicine based on different algorithms

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Chinese journal of integrative medicine 2016年 2016 Apr 4页

作者： Fu-Feng Li Rui Zhou Guo-Zheng Li Rui-Wei Zhao Four Diagnosis Laboratory of Traditional Chinese Medicine Shanghai University of Traditional Chinese Medicine Shanghai 201203 China. li_fufeng@***. Four Diagnosis Laboratory of Traditional Chinese Medicine Shanghai University of Traditional Chinese Medicine Shanghai 201203 China. The Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Control Science & Engineering Tongji University Shanghai 201804 China.

OBJECTIVE:To design a face gloss classification model and to provide an automatic and quantitative approach for the diagnosis of Chinese medicine (CM) based on the face images. METHODS:To classify the face gloss images into two groups (gloss and non-gloss), feature extraction methods were applied to the original images. The original images were supposed to obtain a more ideal representation in which gloss information was better revealed in four color spaces [including red, green, blue (RGB), hue, saturation, value (HSV), Gray and lab]. Principal component analysis (PCA), 2-dimensional PCA (2DPCA), 2-directional 2-dimensional PCA [(2D)PCA], linear discriminant analysis (LDA), 2-dimensional LDA (2DLDA), and partial least squares (PLS) were used as the feature extraction methods of face gloss. k nearest neighbor was used as the classifification method. RESULTS:All the six feature extraction methods were useful in extracting information of face gloss, especially LDA, which had the best prediction accuracy in the 4 color spaces. The average accuracy of LDA in the lab was 7%-10% higher than that of PCA, 2DPCA, (2D)PCA and 2DLDA P<0.05). The prediction accuracy of LDA reached 98% in the lab color space and showed practical usage in clinical diagnosis. The consistent rate between the CM experts and the facial diagnosis system was 81%. CONCLUSION:A computer-assisted classifification model was designed to provide an automatic and quantitative approach for the gloss diagnosis of CM based on the face images.

关键词： Chinese medicine computer-assisted face gloss diagnosis feature extraction image processing

New trust model based on recommendation evidence for P2P networks

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Jisuanji Xuebao/Chinese Journal of Computers 2008年第2期31卷 270-281页

作者： Tian, Chun-Qi Zou, Shi-Hong Wang, Wen-Dong Cheng, Shi-Duan Key Laboratory of Embedded System and Service Computing Tongji University Shanghai 201804 China Broadband Net-work Research Center State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing 100876 China

A novel trust model for P2P networks, namely RETM, a Recommendation Evidence based Trust Model, is presented in this paper. It solves some problems, for instance, not invalidly aggregating incompatible recommendation information and dealing with uncertainty of information in the reputation-based P2P trust model. Before combining the evidences, RETM will filter out noisy recommendation information, and moreover the method makes RETM more robust. In addition a feedback-based probabilistic searching algorithm is proposed to find the recommendation information, which improves the searching success rate and lowers the network traffic. Theoretical analyses and experimental results show that, compared to the current some trust models, the proposed model RETM has advantages in modeling dynamic trust relationship and aggregating recommendation information, moreover, is more robust on trust security problems and more advanced in successful transaction rate.

关键词： Internet

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arXiv 2017年

作者： Lang, Guangming School of Mathematics and Statistics Changsha University of Science and Technology Changsha Hunan410114 China Department of Computer Science and Technology Tongji University Shanghai201804 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China College of Mathematics and Econometrics Hunan University Changsha Hunan410004 China

In practice, there are many dynamic covering decision information systems, and knowledge reduction of dynamic covering decision information systems is a significant challenge of covering-based rough sets. In this paper, we first study mechanisms of constructing attribute reducts for consistent covering decision information systems when adding objects using related families. We also employ examples to illustrate how to construct attribute reducts of consistent covering decision information systems when adding objects. Then we investigate mechanisms of constructing attribute reducts for consistent covering decision information systems when deleting objects using related families. We also employ examples to illustrate how to construct attribute reducts of consistent covering decision information systems when deleting objects. Finally, the experimental results illustrates that the related family-based methods are effective to perform attribute reduction of dynamic covering decision information systems when object sets are varying with time. Copyright © 2017, The Authors. All rights reserved.

关键词： Rough set theory

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arXiv 2017年

作者： Lang, Guangming School of Mathematics and Statistics Changsha University of Science and Technology Changsha Hunan410114 China Department of Computer Science and Technology Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China College of Mathematics and Econometrics Hunan University Changsha Hunan410004 China

In practical situations, there are many dynamic covering information systems with variations of attributes, but there are few studies on related family-based attribute reduction of dynamic covering information systems. In this paper, we first investigate updated mechanisms of constructing attribute reducts for consistent and inconsistent covering information systems when varying attribute sets by using related families. Then we employ examples to illustrate how to compute attribute reducts of dynamic covering information systems with variations of attribute sets. Finally, the experimental results illustrates that the related family-based methods are effective to perform attribute reduction of dynamic covering information systems when attribute sets are varying with time. Copyright © 2017, The Authors. All rights reserved.

关键词： Information use

Incorporation of Ordinal Optimization into Learning Automata for High Learning Efficiency

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Incorporation of Ordinal Optimization into Learning Automata...

IEEE International Conference on Automation Science and Engineering

作者： JunQi Zhang Cheng Wang Di Zang MengChu Zhou Department of Computer Science and Technology the Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Department of Electrical and Computer Engineering New Jersey Institute of Technology

ISBN: (纸本)9781467381840

Learning automata (LA) represent important leaning mechanisms with applications in automated system design, biological system modeling, computer vision, and transportation. They play the critical roles in modeling a process as well as generating the appropriate signal to control it. They update their action probabilities in accordance with the inputs received from the environment and can improve their own performance during operations. The action probability vector in LA takes charge of two functions: 1) The cost of convergence, i.e., the size of sampling budget;2) The allocation of sampling budget among actions to identify the optimal one. These two intertwined functions lead to a problem: The sampling budget mostly goes to the currently estimated optimal action due to its high action probability regardless whether it can help identify the real optimal action or not. This work proposes a new class of LA that separates the allocation of sampling budget from the action probability vector. It uses the action probability vector to determine the size of sampling budget and then uses Optimal computing Budget Allocation (OCBA) to accomplish the allocation of sampling budget in a way that maximizes the probability of identifying the true optimal action. Simulation results verify its significant speedup ranging from 10.93% to 65.94% over the best existing LA algorithms.

关键词： Convergence Optimization Resource management Learning automata Computational modeling Pursuit algorithms Accuracy