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Cerebral Microbleeds Detection Based on 3D Convolutional Neural Network 1

3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020

作者： Zhao, Meng Jin, Chen Jin, Lingmin Teng, Shenghua Li, Zuoyong College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (数字)9783030622237

ISBN: (纸本)9783030622220

Cerebral microbleeds (CMBs) are important imaging and diagnostic biomarkers for cerebrovascular diseases and cognitive dysfunctions. Reliable detection of the location and amount of CMBs in brain tissue is crucial for the diagnosis, prevention and treatment of related diseases, where traditional Convolutional Neural Network (CNN) has been applied but may fail to achieve high enough detection accuracy. To alleviate this issue, we utilize 3D Fully Convolutional Networks (FCN) and 3D AlexNet to establish a cascade coarse-to-fine detection manner. Specifically, CMBs candidates are first screened out using 3D FCN, followed by 3D AlexNet which extracts the spatial features of CMBs and distinguishes the false positive samples from candidate regions. Experimental results show that the proposed method can realize precise detection of CMBs in magnetic resonance images (MRI) by improving detection sensitivity and reducing false positive samples. © 2020, Springer Nature Switzerland AG.

关键词： Magnetic resonance

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Using quantum thinking to construct a musical appreciation system 2nd

Using quantum thinking to construct a musical appreciation s...

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2nd International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2018

作者： Peng, Mei Gangyi, Ding Qiankun, Jin Fuquan, Zhang School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China

ISBN: (纸本)9783030045845

With the richness of material and cultural life, participating in music activities and enjoying music works has become an indispensable activity in people’s leisure time. The rich emotions in music have different performances according to individual differences. After the composers create the music, the performer’s deductive processing and then the emotion is sublimated by the viewers finally. This paper proposes a new emotional system for basic emotional joy, anger, sadness, joy, etc., and interprets the superposition state of emotional expression in detail. The paper believes that the generation of consciousness and the resonance of thinking are precisely the result of the quantum collapse of mutual entanglement. © Springer Nature Switzerland AG 2019.

关键词： Music

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An Encoder-Decoder Network with Residual Convolution for Leukocyte Image Segmentation 7

An Encoder-Decoder Network with Residual Convolution for Leu...

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7th International Conference on information Science and control Engineering, ICISCE 2020

作者： Chen, Yuanyuan Teng, Shenghua Li, Zuoyong Zhang, Fuquan Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China Shandong University of Science and Technology College of Electronics and Information Engineering Qingdao266590 China

ISBN: (纸本)9781728164069

The appearance analysis and counting of peripheral blood leukocytes can assist the diagnosis of blood diseases such as leukemia. Therefore, it is necessary to automatically extract leukocytes from blood smear images. In this paper, we propose an end-to-end peripheral blood leukocyte image segmentation method based on fully convolutional neural network, which can not only encode multi-scale contextual information, but also capture clear boundary information through decoding. Specifically, the proposed method first utilizes the context-aware feature encoder with residual convolutions to extract a set of multi-scale features. Then, a refinement module of parallel dilated convolutions with multiple dilation rates for expanding the receptive field of the feature maps is introduced to aggregate contextual information. Finally, clear prediction results are reconstructed by the context-aware feature decoder and skip connections. We demonstrate the effectiveness of the proposed method on two publicly available datasets. © 2020 IEEE.

关键词： Convolution

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Multi-Attention Gate Based U-net for Retinal Vessel Segmentation 12

Multi-Attention Gate Based U-net for Retinal Vessel Segmenta...

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12th International Conference on information Technology in Medicine and Education, ITME 2022

作者： Zhu, Yu Li, Rui Teng, Shenghua Cao, Xinrong Shandong University of Science and Technology College of Electronic and Information Engineering Qingdao266590 China College of Computer and Control Engineering Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China

ISBN: (纸本)9798350310153

Retinal blood vessel segmentation images can be used to detect and evaluate various cardiovascular and ophthalmic diseases. However, due to the intricate vessel structures and blurred boundaries of vessels, it is a huge challenge to efficiently and accurately segment blood vessels. To deal with the above problems, this paper improves on the U-net by firstly using multi-scale feature convolution with kernels of varying size for feature extraction. Second, a non-local attention mechanism is applied to obtain richer global semantic information. Then multi-attention gate is used in the skip connection part by inputting feature maps of various scales and dimensions and selectively learning the interrelated regions, which improves the segmentation ability of the network model for the tiny structure of blood vessels. Quantitative and qualitative experimental results on two public datasets, DRIVE and CHASE-DB1, demonstrate the effectiveness of the proposed method. © 2022 IEEE.

关键词： Blood vessels

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Liver Tumor Segmentation of CT Image by Using Deep Fully Convolutional Network 1

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3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020

作者： Jin, Lingmin Ma, Rui Zhao, Meng Teng, Shenghua Li, Zuoyong College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (数字)9783030622237

ISBN: (纸本)9783030622220

Accurate segmentation of liver tumors is an important guarantee for the success of liver cancer surgery, where convolutional network has been a type of popular method. However, the performance of the traditional convolutional network is limited by the network depth. To improve the accuracy of liver tumor segmentation, we propose a cascaded deep fully convolutional network (DFCN) which uses ResNet as the basis network followed by side output layer in the upsampling stage to fuse multi-scale image features. For better localizing the liver tumors, the segmentation result is further refined by a fully connected conditional random field. Experimental results show that the proposed method achieves higher segmentation accuracy than several state-of-the-art methods. © 2020, Springer Nature Switzerland AG.

关键词： Convolution

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A Network Combining CNN and Transformer for Blind Image Super-Resolution 12

A Network Combining CNN and Transformer for Blind Image Supe...

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12th International Conference on information Technology in Medicine and Education, ITME 2022

作者： Zhang, Shuhao Li, Zuoyong Teng, Shenghua Zeng, Kun Shandong University of Science and Technology College of Electronic and Information Engineering Qingdao266590 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China

ISBN: (纸本)9798350310153

Blind super-resolution (SR) requires not only estimating blur kernel, but also super-resolving low-resolution image based on estimated blur kernel. Most blind SR methods use convolutional neural networks (CNNs) for kernel estimation, which cannot exploit long-range dependency within image domain, thus failing to predict blur kernel accurately. To address this issue, we propose a network combining CNN and transformer named NCCT for kernel estimation. By modeling local and non-local image priors simultaneously, NCCT outperforms other blind SR methods in terms of kernel estimation accuracy. Moreover, we design a network module named RRFDB for constructing lightweight blind SR network, which runs faster and achieves comparative SR performance with fewer parameters compared with other state-of-the-art blind SR methods. © 2022 IEEE.

关键词： Optical resolving power

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Research on the Security Defense Strategy of Smart City's Substitution Computer Network in Big Data 5

Research on the Security Defense Strategy of Smart City's Su...

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5th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2021

作者： Ye, Yuguang Quanzhou Normal University fujian Quanzhou362000 China Fujian Provincial Key Laboratory of Data Intensive Computing fujian Quanzhou362000 China Key Laboratory of Intelligent Computing and Information Processing Fujian Province University fujian Quanzhou362000 China

ISBN: (纸本)9781665435246

With the rapid development of the information technology era, the era of big data has also arrived. While computer networks are promoting the prosperity and development of society, their applications have become more extensive and in-depth. Smart city video surveillance systems have entered an era of networked surveillance and business integration. The problems are also endless. This article discusses computer network security in the era of big data, hoping to help strengthen the security of computer networks in our country. This paper studies the computer network security prevention strategies of smart cities in the era of big data. © 2021 IEEE.

关键词： Big data

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Few-Shot Class-Incremental Semantic Segmentation via Pseudo-Labeling and Knowledge Distillation 4

Few-Shot Class-Incremental Semantic Segmentation via Pseudo-...

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4th International Conference on information Science, Parallel and Distributed Systems, ISPDS 2023

作者： Jiang, Chengjia Wang, Tao Li, Sien Wang, Jinyang Wang, Shirui Antoniou, Antonios Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Fujian Education Institutions Wuyi University The Key Laboratory of Cognitive Computing and Intelligent Information Processing Wuyishan China College of Computer and Data Science Fuzhou University Fuzhou China European University Cyprus Department of Computer Science and Engineering Nicosia Cyprus

ISBN: (纸本)9798350337181

We address the problem of learning new classes for semantic segmentation models from few examples, which is challenging because of the following two reasons. Firstly, it is difficult to learn from limited novel data to capture the underlying class distribution. Secondly, it is challenging to retain knowledge for existing classes and to avoid catastrophic forgetting. For learning from limited data, we propose a pseudo-labeling strategy to augment the few-shot training annotations in order to learn novel classes more effectively. Given only one or a few images labeled with the novel classes and a much larger set of unlabeled images, we transfer the knowledge from labeled images to unlabeled images with a coarse-to-fine pseudo-labeling approach in two steps. Specifically, we first match each labeled image to its nearest neighbors in the unlabeled image set at the scene level, in order to obtain images with a similar scene layout. This is followed by obtaining pseudo-labels within this neighborhood by applying classifiers learned on the few-shot annotations. In addition, we use knowledge distillation on both labeled and unlabeled data to retain knowledge on existing classes. We integrate the above steps into a single convolutional neural network with a unified learning objective. Extensive experiments on the Cityscapes and KITTI datasets validate the efficacy of the proposed approach in the self-driving domain. Code is available from https://***/ChasonJiang/FSCILSS. © 2023 IEEE.

关键词： Distillation

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Runtime model based approach to IoT application development

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Frontiers of Computer Science 2015年第4期9卷 540-553页

作者： Xing CHEN College of Mathematics and Computer Science Fuzhou University Fuzhou 350116 China Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou 350116 China Key Laboratory of High Confidence Software Technologies (Ministry of Education) Beijing 100871 China School of Electronics Engineering and Computer Science Peking University Beijing 100871 China

The internet of things （loT） attracts great interest in many application domains concerned with monitoring and ：ontrol of physical phenomena. However, application devel- opment is still one of the main hurdles to a wide adoption of IoT technology. Application development is done at a low level, very close to the operating system and requires pro- grammers to focus on low-level system issues. The under- lying APIs can be very complicated and the amount of data collected can be huge. This can be very hard to deal with as a developer. In this paper, we present a runtime model based approach to IoT application development. First, the manage- ability of sensor devices is abstracted as runtime models that are automatically connected with the corresponding systems. Second, a customized model is constructed according to a personalized application scenario and the synchronization be- tween the customized model and sensor device runtime mod- els is ensured through model transformation. Thus, all the application logic can be carried out by executing programs on the customized model. An experiment on a real-world ap- plication scenario demonstrates the feasibility, effectiveness, and benefits of the new approach to IoT application develop- ment.

关键词： IoT application development software architec-ture models at runtime

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A Method for Precise Positioning and Rapid Correction of Blue License Plate 15th

A Method for Precise Positioning and Rapid Correction of Blu...

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15th International Conference on intelligent information Hiding and Multimedia Signal processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of information Technology, Applications and Tools, FITAT 2019

作者： Wu, Jiawei Yu, Zhaochai Zhang, Zuchang Li, Zuoyong Liu, Weina Yu, Jiale College of Computer and Control Engineering Minjiang University Fuzhou350121 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China Department of Computer Engineering Fujian Polytechnic of Information Technology Fuzhou350003 China

ISBN: (纸本)9789811397097

To alleviate the problems of slow speed and weak correction ability of existing license plate correction methods under complex conditions, this paper presents a faster license plate positioning method based on the color component combination and color region fusion and develops a more accurate correction algorithm of blue license plate using probabilistic Hough transform and perspective transform. The proposed methods utilize the characteristics of white characters on the blue background of the Chinese license plate. Color component combination in HSV and RGB color spaces and image thresholding are first performed to obtain the background region of the blue license plate and its character region. Then, both regions are fused to obtain complete and accurate license plate region. And finally, edge detection, probabilistic Hough transform, and perspective transform are performed to achieve rapid license plate correction. Experimental results show that average correction time of blue license plate obtained by the proposed method is 0.023 s, and the average correction rate is 95.0%. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Hough transforms

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