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Federated Hybrid-Supervised Learning for Universal Medical image Segmentation

Federated Hybrid-Supervised Learning for Universal Medical I...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Shenhai Zheng Sian Wen Congyu Li Qing Chen Laquan Li College of Computer Science and Technology Chongqing University of Posts and Telecommunications China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education China School of Science Chongqing University of Posts and Telecommunications China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Federated Learning (FL) is an advanced technology that tackles the challenge of blocked data arising from privacy concerns, enabling the training of deep learning models without the need for data sharing. However, FL faces difficulties with heterogeneous data and limited annotations in medical image segmentation. Motivated by this discovery, this study proposes a novel hybrid-supervised federated learning method (FedSLAG) that explores various types of annotations in medical imaging. To focus more on weakly-supervised and unsupervised scenarios within hybrid-supervised learning, a federated Gaussian enhancement module was proposed for heterogeneous sparse annotations (points and scribbles). The feature extraction module combines the features of multiple weakly-supervised clients and establishes the correlation of similar pixels, thus making up for the deficiency of scarce annotations and the insufficient feature extraction capability of a single machine. Then, a two-stage broadcast mechanism based on supervision sparsity was proposed to alleviate optimization deviation in local models. Experiments on breast tumor and skin lesion segmentation tasks demonstrate significant efficiency gains as well as highly competitive segmentation accuracy in many hybrid-supervised situations. Our codes are available at: https://***/TrivenDev/FedSLAG.

关键词： Training image segmentation Breast tumors Annotations Federated learning Feature extraction Skin Lesions Speech processing Biomedical imaging

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Research on the motion law of rod pumps based on digital twin technology 3

Research on the motion law of rod pumps based on digital twi...

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3rd international conference on Intelligent Control, Measurement and signal Processing and Intelligent Oil Field, ICMSP 2021

作者： Shen, Fei Ren, Shuang Shuang Zhang, Xiang Yang Luo, Hong Wu Feng, Min Chao Fu, Zhan Bao Northwest Branch of China Petroleum Exploration and Development Research Institute Gansu Province Lanzhou730000 China Key Laboratory of Internet of Things CNPC

ISBN: (纸本)9781665437158

In the process of rod pump lifting, the study on the movement law of rod pump and the distribution of liquid pressure and velocity in the valve barrel of the rod pump is an important basis for judging whether the oil well is normal or not and putting forward effective measures. This paper uses the simulation software platform, combined with the physical model of the laboratory, digital modeling. At the same time, a digital twinning method based on digital twinning model is proposed to analyze the movement law of rod pump. This method can solve the transparency of the production site, and can also make quantitative analysis, draw conclusions of construction significance, and guide the site effectively. © 2021 ieee.

关键词： Computer software

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Exploration of CNN Parameters to Measure Performance of LeNet-5 Architecture in Toraja Carving Classification

Exploration of CNN Parameters to Measure Performance of LeNe...

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ieee international conference on signal and image Processing Applications (ICSIPA)

作者： Herman Haidawati Nasir Megat Norulazmi Megat Mohamed Noor Tasrif Hasanuddin Dolly Indra Hence Beedwel Lumentut Faculty of Computer Science Universitas Mus lim Indonesia Makassar Indonesia Computer Engineering Technology MIIT Universiti Kuala Lumpur Kuala Lumpur Malaysia Computer Engineering STMIK Agamua Wamena Wamena Indonesia

ISBN: (数字)9798350352368

ISBN: (纸本)9798350352375

Toraja is a part of Indonesia located on the island of Sulawesi. One form of Toraja culture is carving art made from wood, bamboo or stone. There are around 70 types of Toraja carving motifs. With so many motifs and some motifs being almost similar to each other, it can make it difficult for the public and tourists to know the name of the motif. This research focuses on classifying Toraja carving images using Convolutional Neural Network (CNN) and measuring the performance of the LeN et-5 architecture. The number of Toraja carving motifs used in this research is 7 and the data is 700, where each motif is represented by 100 data. In testing, 3 explorations were carried out, namely dividing training and validation data, Batch Size values, and Target Size values. based on test results, the 70:30 data division provides the highest level of accuracy compared to other data divisions. Increasing the Batch Size value has a negative impact on the level of accuracy. On the other hand, increasing the Target Size value has a positive impact on the accuracy value. The best Batch Size and Target Size values in this study were 32 and 256x256 with respective accuracies of 50.67% and 69.33%.

关键词： Training Accuracy Art image processing Training data Convolutional neural networks Bamboo

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Graph Anomaly Detection via Multi-Scale Reconstruction of Graph Encoder-Decoder Networks

Graph Anomaly Detection via Multi-Scale Reconstruction of Gr...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Jiaqi Yu Hong Yang Peng Zhang Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Existing unsupervised graph anomaly detection (GAD) methods can be categorized into reconstruction based methods and contrastive learning based methods. The principle of reconstruction methods is to capture anomalous nodes based on data reconstruction errors. However, existing reconstruction methods are limited to a single scale reconstruction of graph structures, typically predicting edges solely based on direct connections, without considering the rich contextual information within node neighborhoods. Moreover, reconstructing original low-density inputs usually introduce noise, where the reconstruction model may overfit the anomalous data. To this end, we propose a new reconstruction based graph anomaly detection method (GADMR for short). GADMR introduces a multi-scale reconstruction, which contains both structure and feature levels. From structure level, we use a node-subgraph reconstruction method to capture the relationship between a node and their neighboring subgraphs at multiple scales. From feature level, we employ a multi-layer representation reconstruction method to facilitate the evaluation of discrepancies in high-level feature representations. Extensive experiments on six benchmark datasets show that GADMR outperforms popular anomaly detection methods.

关键词： image edge detection Noise Neural networks Contrastive learning Reconstruction algorithms Data models Decoding Speech processing Anomaly detection Periodic structures

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DeepShield: Lightweight Privacy-Preserving Inference for Real-Time IoT Botnet Detection

DeepShield: Lightweight Privacy-Preserving Inference for Rea...

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ieee international SOC conference

作者： Sabbir Ahmed Khan Zhuoran Li Woosub Jung Yizhou Feng Dan Zhao Chunsheng Xin Gang Zhou Old Dominion University The University of Arizona Towson University William & Mary

ISBN: (数字)9798350377569

ISBN: (纸本)9798350377576

This paper presents a secure convolutional neural network (CNN) based IoT botnet detection system by leveraging the fact that malware execution during various operational phases of botnet attack shows distinctive power consumption patterns. A key challenge is how to effectively unfold the details of malicious activities executed on IoT devices to enable real-time detection of botnet infection, minimizing the loss of botnet attacks. We therefore propose DeepShield, a novel lightweight online CNN model for real-time privacy-preserving feature extraction and classification based on edge computing. The approach lies in the key novelty of a hybrid cryptographic protocol that offloads the majority of online computation to the edge and enables secret-sharing collaborative computation between the smart auditor and edge server. It takes the most expensive computation of homomorphic operations offline, lightening online secure interaction. Through theoretical analysis and empirical experiments, we demonstrate that DeepShield enables secure, high-accuracy, real-time, and scalable botnet infection detection.

关键词： Accuracy Botnet image edge detection Computational modeling Feature extraction Real-time systems System-on-chip internet of Things Convolutional neural networks Cryptographic protocols

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Reversible Data Hiding Using Convolutional Neural Network and Digital signal Processing Techniques 12

Reversible Data Hiding Using Convolutional Neural Network an...

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12th international conference on Information technology in Medicine and Education, ITME 2022

作者： Qingmei, Peng Shaozi, Li Yaojin, Lin Xiehua, Yu University of Fujian Province Key Laboratory of Big Data and Artificial Intelligence MinNan Science and Technology University Quanzhou China Xiamen University School of Informatics Xiamen China Minnan Normal University School of Computer Science Lab of Data Science and Intelligence Application Zhangzhou China

ISBN: (纸本)9798350310153

Reversible data hiding is a data hiding process in which data is encrypted into an image as a means of authentication, and hidden data is used to hide the original image, and RDH is often used in areas where data authenticity and data preservation are important, such as legal/criminal investigations, medical images. In this study, this paper designed a novel reversible data hiding system that utilizes the Steghide algorithm to encrypt images and hide data, as well as an artificial neural network to recover embedded text and structure. based on the analysis of the test results, it was concluded that the RDH scheme proposed in this article has the same or higher system accuracy and reliability as the existing RDH scheme. To further refine the system results in future studies, this paper proposes to increase the number of training iterations and the number of images in the training dataset to further improve the accuracy and reliability of the development. © 2022 ieee.

关键词： Authentication

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OffloaDNN: Shaping DNNs for Scalable Offloading of Computer Vision Tasks at the Edge

OffloaDNN: Shaping DNNs for Scalable Offloading of Computer ...

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international conference on Distributed Computing systems

作者： Corrado Puligheddu Nancy Varshney Tanzil Hassan Jonathan Ashdown Francesco Restuccia Carla Fabiana Chiasserini Politecnico di Torino Italy Institute for the Wireless Internet of Things Northeastern University United States Air Force Research Laboratory United States Chalmers University of Technology Sweden

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Emerging mobile applications often require the execution of computer vision (CV) tasks based on compute-and memory-intensive deep neural networks (DNNs). Although offloading CV tasks to edge servers can decrease resource consumption at the mobile devices, it poses the challenge of handling multiple concurrent tasks with limited computing and memory capacity. In stark opposition with the existing state of the art, we tackle this challenge by jointly optimizing (i) the utilization of resources at the edge, among which memory - so far widely overlooked - and the radio resources used for task offloading; (ii) which and how many offloaded tasks should be executed; and (iii) the structure of the DNNs. First, we formulate the DNN for scalable Offloading of Tasks (DOT) problem, prove that it is NP-hard, and envision a weighted-tree-based heuristic solution, named OffloaDNN, that efficiently solves the DOT problem. We evaluate OffloaDNN through extensive numerical analysis using state-of-the-art image classification ResNet-18, as well as real-world experiments on the Colosseum emulator. The numerical results show that, in small-scale scenarios, OffloaDNN matches the optimum very closely, and, in larger-scale scenarios, increases the number of admitted offloaded tasks by 26.9 % with respect to the state of the art, while saving 82.5 % memory and 77.4% per-inference computing time. The numerical results are confirmed by the real-world validation on Colosseum.

关键词： Training Computer vision Technological innovation Accuracy Memory management Artificial neural networks US Department of Transportation

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Real-Time Video Management System for Robotic Waste Sorting and Recycling Using IoT and Machine Learning 2

Real-Time Video Management System for Robotic Waste Sorting ...

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2nd international conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Raman, Ramakrishnan Meenakshi, R. Latha, R. Rawandale, Chandrashekhar J. Jayaprakash, S. Srinivasan, C. Maharashtra Pune India Department of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Tamil Nadu Trichy India Symbiosis Law School Uttar Pradesh Noida India Maharashtra Pune India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

The system automated robotic for waste sorting and recycling by identifying and categorizing various forms of waste. Traditional sorting of waste takes time and is prone to human mistakes. This solution uses IoT and machine learning to overcome these restrictions and improve waste management efficiency. The system begins with waste-collecting area sensors and cameras. The sensors record waste item weight, size, and color, while the cameras record images. The Raspberry Pi, a small image-processing single-board computer, processes sensor and camera data. Raspberry Pi transfers processed data to the cloud for analysis and decision-making. The Support vector machine (SVM) method classifies waste in the cloud using machine learning. A dataset of waste categories trains the SVM algorithm to discriminate recyclable from non-recyclable materials. The system controls robotic arms or conveyor belts to segregate waste after classification. The robotic arms effectively classify garbage into containers. Real-time video management ensures fast, precise waste sorting, enhancing recycling efficiency. The system can gather data from many garbage-collecting regions concurrently. The cloud architecture enables real-time data processing and analysis. SVM-based machine learning techniques improve waste categorization accuracy, improving trash sorting. Waste sorting and recycling using the real-time video management system is novel. IoT, machine learning, and cloud computing facilitate robotic waste segregation. The method might improve waste management and environmental conservation. Optimizing waste management operations, reducing manual labor, and promoting sustainable methods. © 2023 ieee.

关键词： Cloud internet of Things Recycling Sensors SVM Algorithm Waste Sorting

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A Strategy to Select the Feature Points for Robust Zero-Watermarking Algorithm

A Strategy to Select the Feature Points for Robust Zero-Wate...

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ieee international conference on Research, Innovation and Vision for the Future

作者： Pham Thai Hung Ta Minh Thanh Le Quy Don Technical University Ha Noi Vietnam

ISBN: (数字)9798331505073

ISBN: (纸本)9798331505080

Several watermarking techniques are proposed to protect the copyrights of digital contents. However, most of the current watermarking algorithms neglect the need of real time and resisting malicious geometric attacks. Our paper proposes a robust color image zero-watermarking scheme based on the discrete wavelet transform (DWT) and discrete cosine transform (DCT) domain with significant feature points matching for automatic rotation, scaling, translation factors in order to restore the attacked images before extracting zero-watermark. Firstly, the original image is separated into three components (Y, Cr, Cb), Then, the Y-component is performed by DWT, afterwards its LL3 is transformed with DCT. The DCT image is binarized in order to generate the mastershare. Moreover, Arnold transform is employed to scramble the watermark logo to enhance the security of the algorithm. Finally, we obtain the ownership share by performing the exclusive-or (XOR) operation between the mastershare and the scrambled watermark image, and the ownership share is stored in the copyright database for copyright authentication. In order to recover geometric attacks, we employ the robust feature points matching technique to correction algorithm without using the original image to accurately correct the attacked image. The robustness of the our proposed algorithm for imaging processes is analyzed, and the results show that the proposed algorithm is robust to common signal processing such as noise, filtering, JPEG compression, geometric attacks such as rotation, scaling, translation and so on.

关键词： Translation signal processing algorithms Estimation Watermarking Transforms Feature extraction Robustness Real-time systems Discrete wavelet transforms Discrete cosine transforms

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DEEP ACTIVE LEARNING FOR FIBROSIS SEGMENTATION OF CHEST CT SCANS FROM COVID-19 PATIENTS

DEEP ACTIVE LEARNING FOR FIBROSIS SEGMENTATION OF CHEST CT S...

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ieee international conference on image Processing (ICIP)

作者： Liu, Xiaohong Wang, Kai Chen, Ting Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Tsinghua Univ BNRist Beijing Peoples R China Univ Calif San Diego San Diego CA 92103 USA

ISBN: (纸本)9781665441155

During the ongoing COVID-19 outbreak, it is critical to assess patients' disease progression with COVID-19 pneumonia by computed tomography (CT). As most of the works focused on ground-glass opacity and consolidation segmentation of COVID-19 on CT images, lung fibrosis is relatively undervalued and less studied. Automatic segmentation and accurate measurement of lung fibrosis can potentially aid treatment planning for patients of post-COVID-19 pneumonia. However, the lack of sufficient training data hinders the fibrosis segmentation of CT images. Also, redundancy among CT images can reduce annotating efficiency. To address these issues, we propose deep active learning (AL) framework, which consists of a segmentation model called UNet-RGD, and a novel acquisition method named DeepRISS. The segmentation model consists of improved structures of residual blocks, channel gates, and dropout layers. The deep learning-based acquisition method combines uncertainty estimation and clustering for selecting representative and informative samples. Experimental results show that the AL framework can achieve state-of-the-art performance and effectively reduce the number of selected samples, saving the annotation cost by 25% to 44% compared to the non-selective approach.

关键词： chest CT COVID-19 fibrosis image segmentation active learning

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