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TSM: Three-Stream Mix For Unsupervised Medical image Registration

TSM: Three-Stream Mix For Unsupervised Medical Image Registr...

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IEEE International conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhen Gu Daoqiang Zhang Fang Chen Peng Wan Lingyu Chen Nanjing University of Aeronautics and Astronautics Nanjing China

Medical image registration is a crucial preprocessing step in medical image processing. Due to the potential impact of treatments and disease progression on patients' organ geometry, such as in magnetic resonance imaging (MRI) and computed tomography (CT) images, medical image registration is of significant importance for cancer diagnosis, treatment planning, and therapy. Existing methods usually adopt convolutional neural networks and Transformer frameworks, but still face challenges to effectively solve both tissue robustness and registration accuracy. Therefore, we propose TSM, a hybrid Transformer-Convolution model, for non-rigid registration of volumetric medical multi-tissue images. We perform dynamic global filtering convolution in the frequency domain and multi-scale parallel convolution, capturing local tissue structure information; meanwhile, we use a special attention combination mechanism to obtain semantic associations among tissue structures. We evaluate our method on the publicly available LPBA40 and EMPIRE10 challenge datasets. With comparison of the state-of-the-arts, we improve the dice score by 1.2% on the LPBA40 dataset and by 3% on the EMPIRE10 dataset, achieving the best registration results.

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Big Data processing Technology in Film and Television Post Production

Big Data Processing Technology in Film and Television Post P...

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2020 International conference on Artificial Intelligence, Computer Networks and Communications, AICNC 2020

作者： Xu, Qianqian School of Software Engineering Jiangxi University of Software Professional Technology Nanchang China

Nowadays, the wave of digitization, networking and informatization is sweeping the world, which makes the visual image become an important way of communication and transmission of global culture. The purpose of this paper is to analyze the application of big data processing technology in film and television post production. The distributed computing power of MapReduce is used to extract the content features of video images, construct corresponding indexes, and store and retrieve them in HBase database according to the indexes to form a big data processing platform for video images. This paper deeply discusses the framework related theory as the support of big data processing technology in film and television post production, analyzes the common algorithms in nonlinear editing system, discusses the mirror effect monorail and double track special effects in video special effects, and makes the realization effect of each kind of special effects in the space domain, and makes some special effects among them, The implementation of compressed domain is discussed. In this paper, through the application of experimental video big data processing technology to the film and television post production of various possibilities, and draw the following conclusions: in extreme cases, the stunt tree becomes a one-dimensional linear structure, using traditional parallel topology sorting, the device parallelism is 0, and using pipeline scheduling, because of breaking the sequence between frames, can play a relatively large parallelism. © Published under licence by IOP Publishing Ltd.

关键词： Big data

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ColorRL: Reinforced Coloring for End-to-End Instance Segmentation

ColorRL: Reinforced Coloring for End-to-End Instance Segment...

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Tuan, Tran Anh Khoa, Nguyen Tuan Tran Minh Quan Jeong, Won-Ki UNIST Dept Comp Sci & Engn Ulsan South Korea VinBrain Dept Appl Sci Hanoi Vietnam VinUniversity Hanoi Vietnam Korea Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9781665445092

Instance segmentation, the task of identifying and separating each individual object of interest in the image, is one of the actively studied research topics in computer vision. Although many feed-forward networks produce high-quality binary segmentation on different types of images, their final result heavily relies on the post-processing step, which separates instances from the binary mask. In comparison, the existing iterative methods extract a single object at a time using discriminative knowledge-based properties (e.g., shapes, boundaries, etc.) without relying on postprocessing. However, they do not scale well with a large number of objects. To exploit the advantages of conventional sequential segmentation methods without impairing the scalability, we propose a novel iterative deep reinforcement learning agent that learns how to differentiate multiple objects in parallel. By constructing a relational graph between pixels, we design a reward function that encourages separating pixels of different objects and grouping pixels that belong to the same instance. We demonstrate that the proposed method can efficiently perform instance segmentation of many objects without heavy post-processing.

关键词： image segmentation Computer vision Three-dimensional displays Shape Scalability Reinforcement learning Pattern recognition

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Experience Replay as an Effective Strategy for Optimizing Decentralized Federated Learning

Experience Replay as an Effective Strategy for Optimizing De...

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International conference on Computer Vision Workshops (ICCV Workshops)

作者： Matteo Pennisi Federica Proietto Salanitri Giovanni Bellitto Concetto Spampinato Simone Palazzo Bruno Casella Marco Aldinucci PeRCeiVe Lab University of Catania Italy Parallel Computing Group University of Turin Turin Italy

Federated and continual learning are training paradigms addressing data distribution shift in space and time. More specifically, federated learning tackles non-i.i.d data in space as information is distributed in multiple nodes, while continual learning faces with temporal aspect of training as it deals with continuous streams of data. Distribution shifts over space and time is what it happens in real federated learning scenarios that show multiple challenges. First, the federated model needs to learn sequentially while retaining knowledge from the past training rounds. Second, the model has also to deal with concept drift from the distributed data distributions. To address these complexities, we attempt to combine continual and federated learning strategies by proposing a solution inspired by experience replay and generative adversarial concepts for supporting decentralized distributed training. In particular, our approach relies on using limited memory buffers of synthetic privacy-preserving samples and interleaving training on local data and on buffer data. By translating the CL formulation into the task of integrating distributed knowledge with local knowledge, our method enables models to effectively integrate learned representation from local nodes, providing models the capability to generalize across multiple *** test our integrated strategy on two realistic medical image analysis tasks — tuberculosis and melanoma classification — using multiple datasets in order to simulate realistic non-i.i.d. medical data scenarios. Results show that our approach achieves performance comparable to standard (non-federated) learning and significantly outperforms state-of-the-art federated methods in their centralized (thus, more favourable) formulation.

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DSPG: Decentralized Simultaneous Perturbations Gradient Descent Scheme 28

DSPG: Decentralized Simultaneous Perturbations Gradient Desc...

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28th Euromicro International conference on parallel, distributed and Network-Based processing (PDP)

作者： Ramaswamy, Arunselvan Paderborn Univ Dept Elect & Comp Sci Engn D-33098 Paderborn Germany

ISBN: (纸本)9781728165820

distributed descent-based methods are an essential toolset to solving optimization problems in multi-agent system scenarios. Here the agents seek to optimize a global objective function through mutual cooperation. Oftentimes, cooperation is achieved over a wireless communication network that is prone to delays and errors. There are many scenarios wherein the objective function is either non-differentiable or merely observable. In this paper, we present a cross-entropy based distributed stochastic approximation algorithm (SA) that finds a minimum of the objective, using only samples. We call this algorithm Decentralized Simultaneous Perturbation Stochastic Gradient, with Constant Sensitivity Parameters (DSPG). This algorithm is a two fold improvement over the classic Simultaneous Perturbation Stochastic Approximations (SPSA) algorithm. Specifically, DSPG allows for (i) the use of old information from other agents and (ii) easy implementation through the use simple hyper-parameter choices. We analyze the biases and variances that arise due to these two allowances. We show that the biases due to communication delays can be countered by a careful choice of algorithm hyper-parameters. The variance of the gradient estimator and its effect on the rate of convergence is studied. We present numerical results supporting our theory. Finally, we discuss an application to the stochastic consensus problem.

关键词： distributed optimization distributed approximate gradient method cross-entropy based gradient approximation optimization over networks with unbounded delays

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Deep learning methods for malicious URL detection using embedding techniques as Logistic Regression with Lasso penalty and Random Forest

Deep learning methods for malicious URL detection using embe...

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International conference on parallel distributed and Grid Computing (PDGC)

作者： Isha Thakur Kajal Panda Sanjeev Kumar Cyber Security Technology Division Centre for Development of Advanced Computing Mohali India

ISBN: (纸本)9781665454025

In the Internet ecosystem, URLs (Uniform Resource Locators) are widely used to propagate malicious infections through spam, spear-phishing, drive-by-download exploitation, malware embedding, etc. The blacklisting, pattern analysis, and signature-matching approaches are widely used to detect such threats. However, these techniques are very effective in detecting known types of malicious URLs but unable to detect new types of attacks launched by the malicious URLs. Moreover, traditional machine learning-based malicious URL detection is highly dependent upon manual feature engineering, which is a costly approach. , This research study proposes deep learning-based malicious URL detection with less dependent on manual feature engineering to overcome the above-mentioned challenges. The Natural Language processing (NLP) technique, i.e., Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer, along with the N-gram parameter, is used for extracting the features. Afterward, two feature selection techniques as Embedded methods are used - Logistic Regression with L1 (Lasso) penalty and Random Forest Selection. Neural network models such as Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Convolution Neural Networks (CNN) are constructed to detect malicious URLs. The experiment results demonstrate that the DNN model gives the best performance results as 96.95% accuracy, 99% precision, 100% recall, and 99% F1-score with logistic regression along with the L1 (Lasso) penalty as a feature selection method. Finally, this study compares state-of-art implemented deep learning methods for malicious URL detection.

关键词： Uniform resource locators Deep learning Biological system modeling Neural networks Manuals Feature extraction Natural language processing

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image INPAINTING USING parallel NETWORK

IMAGE INPAINTING USING PARALLEL NETWORK

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IEEE International conference on image processing (ICIP)

作者： Deng, Ye Wang, Jinjun Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot 28 West Xianning Rd Xian Shaanxi Peoples R China

ISBN: (纸本)9781728163956

Due to the lack of contextual information and the difficulty to directly learn the distribution of a complete image, existing image inpainting methods always use a two-stages approach to make plausible prediction for missing pixels in a coarse-to-fine manner. In this paper, we propose a novel inpainting method with two parallel pipelines. The first pipeline is a standard image completion path that takes the corrupted image as input and outputs the predicted complete image. The second pipeline exists only during the training phase that inputs a complementary image of the corrupted one and still outputs the same complete image. The two pipelines operate simultaneously, and they share identical encoder and most parameters in the decoder. Furthermore, inspired by VAE, random Gaussian noise are added to the features not only to improve the robustness of the model but also to enable generating diverse and plausible results. We evaluated our model on several public datasets and demonstrated that the proposed method outperforms several state-of-the-arts approaches.

关键词： image Completion parallel Pipelines Complementary images Random Noise

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Enhancing IoT Compliance Checking with distributed Process Mining: A Scalable Framework for Log Data Streams 18th

Enhancing IoT Compliance Checking with Distributed Process ...

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18th China conference on Wireless Sensor Networks, CWSN 2024

作者： Song, Chao Ren, Zheng Hu, Ruilin Lu, Li School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9789819621859

The integration of the Internet of Things (IoT) into various industries has led to an exponential increase in the volume of data generated, posing significant challenges for compliance monitoring. Traditional compliance checking methods are often inadequate due to their inability to handle the high velocity and volume of real-time data streams emanating from IoT devices. In this paper, we introduce a novel distributed computing framework specifically designed to address these challenges by leveraging process mining techniques on IoT log data streams. The proposed framework is built to scale horizontally, allowing for the parallel processing of massive data sets while maintaining real-time performance. It is capable of identifying compliance patterns, detecting deviations, and providing actionable insights to ensure that IoT systems operate within the confines of established regulations and standards. To evaluate the performance and effectiveness of the proposed framework, we conducted extensive experiments using a publicly available industrial IoT log dataset. The results demonstrate the framework’s ability to process large volumes of data in real-time, offering a significant improvement over existing centralized approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data streams

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Semantic Maximum Relevance and Modal Alignment for Cross-Modal Retrieval 22

Semantic Maximum Relevance and Modal Alignment for Cross-Mod...

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Proceedings of the 2022 11th International conference on Computing and Pattern Recognition

作者： Pingping Sun Baohua Qiang Zhiguang Liu Xianyi Yang Guangyong Xi Weigang Liu Ruidong Chen Shihao Zhang Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology China Hebei Key Laboratory of Intelligent Information Perception and Processing The 54th Research Institute of CETC China

ISBN: (纸本)9781450397056

With the increasing abundance of multimedia data resources, researches on mining the relationship between different modalities to achieve refined cross-modal retrieval are gradually emerging. In this paper, we propose a novel Semantic Maximum Relevance and Modal Alignment (SMR-MA) for Cross-Modal Retrieval, which utilizes the pre-trained model with abundant image text information to extract the features of each image text, and further promotes the modal information interaction between the same semantic categories through the modal alignment module and the multi-layer perceptron with shared weights. In addition, multi-modal embedding is distributed to the normalized hypersphere, and angular edge penalty is applied between feature embedding and weight in angular space to maximize the classification boundary, thus increasing both intra-class similarity and inter-class difference. Comprehensive analysis experiments on three benchmark datasets demonstrate that the proposed method has superior performance in cross-modal retrieval tasks and is significantly superior to the state-of-the-art cross-modal retrieval methods.

关键词： Semantic Relevance CLIP Cross-Modal Retrieval Modal Alignment

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Network Coding for Scalable Genomic Data Transmission in Public Health Crises 3

Network Coding for Scalable Genomic Data Transmission in Pub...

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3rd International conference on Disruptive Technologies, ICDT 2025

作者： Alam, Sultan Kumar, Ram Sankat, Monica School of Computing Science and Engineering Vit Bhopal University Sehore466114 India

ISBN: (纸本)9798331519582

Quick genomic data transfer acts as an essential tool that public health teams need during emergencies like the COVID-19 outbreak. Network coding stands as a technology evaluated for improving large-scale genomic data transmissions in distributed networks. The experimental results show that network coding leads to substantial improvement of transmission throughput through reduces times by 47.2% relative to other standard approaches. Network coding technology displays superior error resilience capability because it recovers 92.3% of data at 10% error rate when compared to normal transmission methods which recover only 65.4%. The introduction of slight computational workloads provides high data integrity protection by maintaining reconstruction accuracy above 98.4% and achieving complete integrity for both small and large datasets. The utilization rate of networks can reach 89.5% across multiple server environments. The study demonstrates how network coding technology optimizes genetic data sharing processes by delivering quicker and more resilient and expandable information distribution for genomic research along with public health intervention programs. © 2025 IEEE.

关键词： parallel processing systems

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