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A Multi-staining Digital Pathology image Registration Method Based on Global and Local Computing 27

A Multi-staining Digital Pathology Image Registration Method...

27th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing (SNPD)

作者： Ding, Yi Zhang, Xin Lu, Cheng Commun Univ China Sch Comp & Cyber Sci Beijing Peoples R China Guangdong Prov Peoples Hosp Dept Radiol Guangzhou Peoples R China

ISBN: (纸本)9798350391961;9798350391954

To accurately evaluate the patient's condition, medical workers usually need to register multiple pathological images of the lesion site samples. Using computer technology to assist in registration work can effectively improve the efficiency of doctors analyzing pathological images. One of the most advanced methods currently is the Virtual Alignment of Pathology image Series method, which is a multi-staining digital pathology image registration method that combines global and local calculations. However, this method may encounter certain biases when processing images with significant angle differences. Through a detailed analysis of this method, this article proposes an improvement plan which optimizes the acquisition of non-rigid registration mask images, enabling the method to obtain mask images more reasonably and achieve better registration results for images with significant angle differences. This provides more accurate judgment basis and helps doctors diagnose and develop treatment plans more accurately.

关键词： digital pathological images global rigid registration local non-rigid registration feature point detection

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FERI: Feature Enhancement and Relational Interaction for image-text Matching 30

FERI: Feature Enhancement and Relational Interaction for Ima...

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30th IEEE International Conference on parallel and distributed Systems, ICPADS 2024

作者： Zhang, Yu Zhang, Jianqiang Song, Gongpeng Lu, Qin Zhao, Shuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9798331515966

image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method. © 2024 IEEE.

关键词： cross-modal retrieval feature enhancement image-text matching relationship interaction

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image Compression and Reconstruction Based on Quantum Network

Image Compression and Reconstruction Based on Quantum Networ...

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1st International Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Ji, Xun Liu, Qin Huang, Shan Chen, Andi Wu, Shengjun Nanjing Univ Sch Phys Natl Lab Solid State Microstruct Nanjing 210093 Peoples R China Nanjing Univ Collaborat Innovat Ctr Adv Microstruct Nanjing 210093 Peoples R China Nanjing Univ Inst Brain Sci Nanjing 210023 Peoples R China Nanjing Univ Kuang Yarning Honors Sch Nanjing 210023 Peoples R China Univ Sci & Technol China Hefei Natl Lab Hefei 230088 Peoples R China

ISBN: (纸本)9798350364613;9798350364606

Quantum network is an emerging type of network structure that leverages the principles of quantum mechanics to transmit and process information. Compared with classical data reconstruction algorithms, quantum networks make image reconstruction more efficient and accurate. They can also process more complex image information using fewer bits and faster parallel computing capabilities. Therefore, this paper will discuss image reconstruction methods based on our quantum network and explore their potential applications in image processing. We will introduce the basic structure of the quantum network, the process of image compression and reconstruction, and the specific parameter training method. Through this study, we can achieve a classical image reconstruction accuracy of 97.57%. Our quantum network design will introduce novel ideas and methods for image reconstruction in the future.

关键词： image compression and reconstruction quantum network parameter training

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Improving Visual Question Answering by image Captioning

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IEEE ACCESS 2025年 13卷 46299-46311页

作者： Shao, Xiangjun Dong, Hongsong Wu, Guangsheng Hunan Univ Arts & Sci Sch Comp & Elect Engn Changde 415000 Peoples R China Key Lab Hunan Prov Control Technol Distributed Ele Changde 415000 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Lyuliang Univ Dept Comp Sci Luliang 033000 Peoples R China Xinyu Univ Sch Math & Comp Sci Xinyu 338004 Peoples R China

Visual Question Answering (VQA) is a challenging task that bridges the computer vision and natural language processing communities. It provide natural language answers to questions related to an associated image. Most existing VQA methods focus on the fusion and inference of visual features with the textual question. However, visual features often lack the necessary semantic information required to answer the questions accurately. To address this limitation, we propose a novel approach called Question-Guided parallel Attention (QGPA), which effectively leverages the semantic information provided by an embedded image captioning model to answer related questions. First, we introduce an Attention-Aware (AA) mechanism that extends the traditional attention mechanism, helping to filter out incorrect or irrelevant information during answer prediction. Second, QGPA incorporates AA, which simultaneously utilizes visual features and semantic information from the embedded image captioning model to answer questions. Experiments results demonstrate that the accuracy of "Overall" of our proposed model delivers 72.57% and 72.55% on the test-dev and test-std split set of VQA-v2.0 dataset, respectively, which outperforms most existing VQA methods. The experiment results and ablation studies demonstrate that the proposed method has good performance.

关键词： Visualization Semantics Long short term memory Question answering (information retrieval) Predictive models Accuracy Knowledge based systems Feature extraction Information filters Context modeling Deep learning image captioning multimodal learning visual question answering

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Spatial-Frequency Integration Network with Dual Prompt Learning for Few-shot image Classification 22

Spatial-Frequency Integration Network with Dual Prompt Learn...

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22nd IEEE International Symposium on parallel and distributed processing with Applications, ISPA 2024

作者： Liu, Yi He, Jie Xu, Shoukun Jiang, Guangqi Changzhou University Academy of Computing and Artificial Intelligence Jiangsu Changzhou213164 China

ISBN: (纸本)9798331509712

Few-shot image classification is a challenging task that aims to recognize image classes based on only a few training images. However, existing methods face the following two main challenges: (1) Ignoring the frequency domain information during image feature extraction. (2) It does not take the semantic gap between multiple modalities into consideration, which limits the classification performance. To overcome these limitations, we propose a novel method named Spatial-Frequency Integration Network with Dual Prompt Learning for few-shot image classification. Firstly, we introduce a spatial-frequency integration module that combines spatial domain and low-frequency information to extract discriminative image features from the image modality. Secondly, we design a dual prompting module, which integrates learnable prompts and hand-crafted prompts to improve the generalization of applications to new classes. Thirdly, we propose an image-text interaction module to enhance inter-modal complementary and consistency. Both theoretical and experimental validations confirm the effectiveness of the proposed method in few-shot image classification. © 2024 IEEE.

关键词： Zero-shot learning

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Scalable SubXPCA on a distributed Platform Applied to Classification of Hyperspectral images 8

Scalable SubXPCA on a Distributed Platform Applied to Classi...

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8th IEEE International Conference on Information and Communication Technology, CICT 2024

作者： Rupa, Bogolu Negi, Atul School of Computer and Information Sciences University of Hyderabad India Vardhaman College of Engineering Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331531539

The exponential growth of technological advancements in satellite and airborne remote sensing is giving rise to large volumes of high-dimensional hyperspectral image data. Apache Spark is one of the most popular, extensively used and open-source distributed processing frameworks, which is proven effective in processing large volumes of remotely sensed hyperspectral images in a time-efficient manner. Open-source distributed processing frameworks have proven effective in processing large volumes of remotely sensed hyperspectral images quickly and efficiently. While computational power has been increasing, the rate of data accumulation is more than the processing capabilities. Therefore, more efficient algorithms such as dimensionality reduction are needed to process and get accurate performance for the application. This paper proposes an efficient and parallel spectral dimensionality reduction approach based on feature partitioning principal component analysis called scalable SubXPCA. We implemented scalable SubXPCA on a spark cluster distributed environment. We compared scalable SubXPCA against other distributed feature partitioning and various non-feature partitioning dimensionality reduction methods. Our experiments on different real and synthetic datasets of hyperspectral images confirm that SubXPCA classification performance is not only better than its competitors but also that the running time of SubXPCA is better in distributed processing than serial processing. As the size of the hyperspectral image dataset increased, SubXPCA showed a speed up factor of 5.7× and more in the spark cluster compared to the serial version. © 2024 IEEE.

关键词： Dimensionality reduction

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distributed Real-Time image processing of Formation Flying SAR Based on Embedded GPUs

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 6495-6505页

作者： Yang, Tao Xu, Qingbo Meng, Fanteng Zhang, Shuangxi Xidian Univ Sch Aerosp Sci & Technol Xian 710126 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710126 Peoples R China Northwestern Polytech Univ Sch Elect & Informat Xian 710071 Peoples R China

Formation flying synthetic aperture radar (FF-SAR) systems, as an important development direction of multichannel SAR, can achieve high-resolution wide-swath imaging. Coherently combining data from satellite receivers puts a strain on the traditional real-time processing systems based on individual satellites. Characteristics, such as the power of real-time on-orbit processing platform, must be properly balanced with constrained memory and parallel computational resources. This article proposes a distributed SAR real-time imaging method based on the embedded graphics processing units (GPUs). The parallel computing method of the chirp scaling algorithm is designed based on the parallel programming model of compute unified device architecture, and the optimization methods of memory and performance are proposed for the hardware architecture of embedded GPUs. In particular, the unified memory management method is used to avoid data copying and communication delays between the CPU and GPU. A hardware verification system for distributed SAR real-time imaging processing based on multiple embedded GPUs is constructed. The proposed algorithm takes 5.86 s to process single-precision floating-point complex imaging with a data size of 8192 x 8192 on a single Jetson Nano platform. The actual power consumption is less than 5 W, and the performance-to-power ratio is greater than 1.7%. The experimental results show that the real-time processing method based on the embedded GPUs proposed in this article has high performance and low-power consumption.

关键词： Graphics processing units Imaging Azimuth Synthetic aperture radar Real-time systems Satellites Radar polarimetry Chirp scaling (CS) algorithm distributed architecture embedded graphics processing unit (GPU) on-orbit real-time processing synthetic aperture radar (SAR)

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Low-frequency Radio Interferometric Array Imaging Pipeline Optimization Based on distributed Execution Framework

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Chinese Astronomy and Astrophysics 2024年第2期48卷 389-412页

作者： Yao-jie, W.E.I. Jie-lin, F.U. Bao-qiang, L.A.O. Key Laboratory of Cognitive Radio and Information Processing of the Ministry of Education School of Information and Communication Guilin University of Electronic Science and Technology Guilin 541004 China Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai 200030 China School of Physics and Astronomy Yunnan University Kunming 650500 China

The Square Kilometre Array (SKA) project is an international collaboration to build the world's largest radio telescope, whose sensitivity and measurement speed will be an order of magnitude higher than those of all current radio telescopes. Radio continuum survey is one of the main observation mode of the SKA, and the establishment of a standard map of the survey area based on continuum imaging will provide an important foundation for subsequent astronomical science. The GaLactic and Extragalactic All-sky Murchison Widefield Array survey eXtended (GLEAM-X) is a project of the SKA pilot telescope Murchison Widefield Array (MWA) in 2018—2020. GLEAM-X is a new radio continuum survey project to be carried out with the MWA Phase II expansion array in 2018—2020. The experience of optimizing the imaging pipeline based on the distributed execution framework will help to solve the problem of massive data processing. In this paper, we describe the process steps of GLEAM-X imaging pipeline, integrate and improve it, and realize parallel processing of multiple pipelines on the China SKA Regional Centre Prototype (CSRC-P), and verify the deployment and test the correctness of the imaging pipeline system using GLEAM-X observation data. The GLEAM-X observations were used to validate the deployment of the imaging pipeline system and test its correctness. Then, to optimize the pipelines and improve the processing efficiency, the Data Activated Liu Graph Engine (DALiuGE) was used to integrate the imaging pipelines into the DALiuGE execution framework to automate the distributed parallel processing of the pipelines. Performance tests and results’ analysis show that the optimized imaging pipeline based on the DALiuGE execution framework has better performance, more flexible adaptability, and scalability than the traditional parallel approach, and can support future large-scale continuum imaging experiments during the first phase of SKA commissioning. © 2019

关键词： general image processing—radio continuum instrumentation interferometers—methods interferometric—techniques observational—techniques

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FCTNet: A CNN-Transformer Hybrid for Single Remote Sensing image Super-Resolution 22

FCTNet: A CNN-Transformer Hybrid for Single Remote Sensing I...

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22nd IEEE International Symposium on parallel and distributed processing with Applications, ISPA 2024

作者： Shi, Ning Zhou, Hui Ye, Chunyang Yao, Biyuan Wu, Yilin Li, Bo Hainan University Haikou China

ISBN: (纸本)9798331509712

Convolutional Neural Network (CNN) and Transformer architectures have been extensively applied in the domain of remote sensing image super-resolution. However, to achieve optimal performance, many existing methods are designed with a large number of parameters, thereby increasing the complexity of the model and hindering practical deployment. To address this issue, we propose an innovative model that synergizes CNN and Transformer architectures while employing a minimal number of their respective modules. This approach significantly reduces the parameter count while maintaining superior performance. Firstly, by constructing additional shallow feature representations as input, we enhance the feature extraction capabilities for individual images. Secondly, we utilize residual connections between various modules to integrate multi-scale, high-dimensional feature information, thus ensuring efficient transmission. Finally, the image reconstruction module is employed to restore the high-resolution image. Experimental results show that FCTNet significantly outperforms existing methods while maintaining a substantially lower parameter count, as demonstrated through evaluations on two public datasets. © 2024 IEEE.

关键词： Convolutional neural networks

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High-Precision Encrypted image Retrieval Scheme Using Dual-Stream Convolution Feature Fusion in Cloud Computing 22

High-Precision Encrypted Image Retrieval Scheme Using Dual-S...

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22nd IEEE International Symposium on parallel and distributed processing with Applications, ISPA 2024

作者： Li, Mingyue Zheng, Liudong Liu, Jiahui Du, Ruizhong Jia, Chunfu Hebei University Baoding China Nankai University Tianjin China

ISBN: (纸本)9798331509712

The retrieval of encrypted images in cloud computing is a research hotspot at present. However, the existing schemes have the problem of low image retrieval accuracy since it is difficult to obtain accurate feature information through convolutional neural network from encrypted image. In this paper, a secure image retrieval method using dual-stream convolution structure for feature fusion is proposed. Specifically, the stream cryptographic image containing contour features and the Fourier transform image containing frequency domain features are used as the two inputs of the convolutional network stream, where the weighted average gate function helps to integrate the feature information in these streams to achieve a more comprehensive feature representation and improve the retrieval accuracy. Furthermore, to keep the contour features of the encrypted image, the fuzzy image and Arnold mapping algorithm are selected randomly to encrypt the encrypted image twice. Experimental evaluation on three datasets, MNIST, Fashion-MNIST and CIFAR-100, shows that the proposed encryption method has higher security than AES and stream cipher encryption. In addition, the retrieval average accuracy (mAP) of this scheme is superior to the existing methods. © 2024 IEEE.

关键词： Convolutional neural networks

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