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CoGAP: A Personalized Federated Learning Method Using Collaborative Optimization for Medical image Classification

CoGAP: A Personalized Federated Learning Method Using Collab...

引用

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

作者： Shenhai Zheng Congyu Li Sian Wen Xi Gao Lei Yu College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing China Emergency Department The Second Affiliated Hospital of Chongqing Medical University Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Federated learning (FL) has been widely used in medical image processing to protect data privacy, but it has issues with data heterogeneity. Personalized federated learning have emerged to tackle these issues but often focuses too much on personalized models at the expense of global models. To address these problem, we propose a personalized federated learning method CoGAP, using collaborative optimization to enhance both personalized and global model performance. On the client side, it employs adaptive weight aggregation to initialize personalized models and uses a training strategy based on L 2 regularization. On the server side, it implements a gradient accumulation-based aggregation method. These modules facilitate a collaborative optimization process where the global model guides the personalized models, and the personalized models provide positive feedback to the global model, ultimately leading to mutual improvement for both. We conducted extensive comparative experiments on the OCT2017 dataset, as well as the BloodMnist and PathMnist (subsets from MedMnist), to evaluate both global and personalized models. Results show that CoGAP own a unique strength which not only outperforms other personalized federated learning methods in personalization capability while also remarkably achieving satisfying global generalization. Code is available at https://***/lcyCQUPT/CoGAP.

关键词： Training Adaptation models Federated learning Collaboration signal processing Servers Speech processing Optimization image classification Biomedical imaging

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Subband Fusion based W-GAN for Biomedical image Super-Resolution

Subband Fusion Based W-GAN for Biomedical Image Super-Resolu...

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international conference on Applied Electromagnetics, signal Processing and Communication (AESPC)

作者： Dharitri Tanaya Sahoo Suprava Patnaik Sukant Kumar Sabut School of Electronics Engineering Kalinga Institute of Industrial Technology Bhubaneswar Odisha

ISBN: (数字)9798331528171

ISBN: (纸本)9798331528188

Finding tiny abnormality, textual deviation and other details are crucial for diagnosis in medical images. imageprocessing resulting into super-resolution high quality medical imaging helps clinicians for accurate analysis. Medical images are acquired using different modalities and developing a common method for biomedical images of different modalities is immensely challenging. Popular biomedical imaging modalities, are CT, PETCT, MRI, and ultrasound etc. Super-resolution achieved through iinterpolation-based techniques are often reported for its quality to computational complexity trade-off. Learning based upsampling using transposed convolution and sub-pixel layers are anticipated overcome the shortcomings of interpolation method. SRGAN and ESRGAN models have recently become popular generative models for their performance in superresolution. This paper proposes a novel Wavelet GAN (W-GAN). Proposed method hasbeen tested on medical images from different modalities. Results are compared for the conventional interpolation method, ESRGAN, and the proposed MS-SRGAN. Though Deep Super resolution itself is a multiscale approach, the innovation of the proposed work is decomposing the input low resolution image into subbands by using discrete wavelet transform. Therefore, deep representation along with separated complementary textual and edge features has been proven efficient. Training of GAN models gets streamlined and efficient for images of different resolution and having similar high frequency information. Reconstructed SR image obtained from inverse discrete wavelet transform has better clarity than ground truth image. The parameters like Peak signal to noise ratio (PSNR) and structural index similarity (SSIM) are used to quantify the improvement in super resolute images. In our proposed method we observed that the quantitative result is better in terms of PSNR and SSIM as compared to state of art methods.

关键词： Training Interpolation PSNR Ultrasonic imaging Computational modeling Superresolution Transforms Discrete wavelet transforms Medical diagnostic imaging signal resolution

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9.6 A 1/2.3inch 12.3Mpixel with On-Chip 4.97TOPS/W CNN Processor Back-Illuminated Stacked CMOS image Sensor

9.6 A 1/2.3inch 12.3Mpixel with On-Chip 4.97TOPS/W CNN Proce...

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2021 ieee international Solid-State Circuits conference, ISSCC 2021

作者： Eki, Ryoji Yamada, Satoshi Ozawa, Hiroyuki Kai, Hitoshi Okuike, Kazuyuki Gowtham, Hareesh Nakanishi, Hidetomo Almog, Edan Livne, Yoel Yuval, Gadi Zyss, Eli Izawa, Takashi Sony Semiconductor Solutions Tokyo Japan Sony Semiconductor Israel Hod-Hasharon Israel

ISBN: (纸本)9781728195490

Within the internet of Things (IoT) market, retail, smart city, and so on, the need for camera products which have Artificial Intelligence (AI) processing capabilities is growing. AI processing capability on such edge devices solves some issues of cloud-only computing systems, such as latency, cloud communication, processing cost, and privacy concerns. The market demand for smart cameras with AI processing capabilities includes small size, low cost, low power and ease of installation. However, conventional CMOS image Sensors (CIS) only output the raw data of the captured image. Therefore, when developing a smart camera that has AI processing capabilities, it is necessary to utilize ICs that include an image signal processor (ISP), CNN processing, DRAM and so on. Unfortunately, this results in system solution that is larger, higher power and more costly. © 2021 ieee.

关键词： CMOS integrated circuits

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Research of a Foreign Objects Detection Fusion Algorithm Using Video and LIDAR in FAO System 5

Research of a Foreign Objects Detection Fusion Algorithm Usi...

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5th international conference on Renewable Energy and Power Engineering, REPE 2022

作者： Yu, Qingguang Wang, Shi Zhang, Lintao Tan, Ashton Yuxuan Liu, Youqi Siow, Chengbo Wang, Yujin Cai, Guanzi Tsinghua University Dept. of E. E. Beijing China School of A. E. Tsinghua University Beijing China Tsinghua University Dept. of C. E. Beijing China Tsinghua University Dept. of I. E. Beijing China Beijing Guanfenghang Security Technology Co. Beijing China

ISBN: (数字)9781665482066

ISBN: (纸本)9781665482066

This paper aims to research a fusion algorithm for subway screen doors to make future fully automatic operation (FAO) systems safer and more efficient. This paper adopts video with light detection and ranging (LIDAR) fusion algorithm technology, and proposes a dual-criteria artificial intelligence (AI) strategy using video image recognition and radar point cloud fusion to detect foreign objects in the gap between subway platform doors. A novel approach of cross-stacking and layered installation of sensors is innovatively proposed to realize the function of redundant detection of foreign objects in the gap. Cross-checking improves the reliability of the detection device while 2D sensors are used to achieve 3D detection effects. The developed system provides safety interlocking signals for the subway signal system, delivering alarm information to the integrated monitoring system, then pushes the smart hand ring alarm information to the on-site operators. © 2022 ieee.

关键词： image recognition

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Efficient image Transmission over Cluster-based Cooperative Visual Sensor Networks 6

Efficient Image Transmission over Cluster-Based Cooperative ...

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6th international conference for Convergence in technology (I2CT)

作者： Lakshmi, A. Vijaya Reddy, K. Sripal Vardhaman Coll Engn Dept ECE Hyderabad India

ISBN: (纸本)9781728188768

Traditional dual-hop cooperative communication may not suitable for the clustered based cooperative wireless networks, since the intermediate nodes with improper channel condition may become the bottleneck of the overall communication. To address this problem, a cluster-based cooperative framework was presented in this paper. In this framework, all the candidate relays are divided into two clusters: relays near to the source (called source cluster) and nodes near to the destination (named as destination cluster). Further, to improve the performance, an algorithm for the efficient image transmission methodology was presented. Generally, in image transmission, Discrete Wavelet Transforms (DWT) is widely used for image compression. Since the DWT is a convolution-based approach, designing the hardware circuit for floating-point numbers is difficult. Here, we adopt lifting based discrete wavelet transform to reduce the hardware implementation complexity for the compression. Through the simulation results, we show the performance improvement of the proposed method against state-of-art approaches in terms of peak signal to noise ratio, mean square error, structural similarity index and bit error rate.

关键词： Cooperative communication Visual sensor networks Cluster based cooperative wireless networks Discrete wavelet transform

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Modality Modulation and Dual Consistency for Multi-Modality Semi-Supervised Medical image Segmentation

Modality Modulation and Dual Consistency for Multi-Modality ...

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

作者： Yingyu Chen Ziyuan Yang Deng Xiong Yi Zhang College of Computer Science Sichuan University Chengdu China School of Cyber Science and Engineering Sichuan University Chengdu China Computer Science Department Stevens Institute of Technology Hoboken NJ USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multi-modality (MM) semi-supervised learning (SSL) based medical image segmentation has recently gained increasing attention due to its ability to utilize MM data and low dependency on labeled images. However, current MM-SSL methods face two major challenges: (1) Complex network designs make it difficult to apply these methods to scenarios involving more than two modalities. (2) The use of generative methods to leverage unlabeled data may not be reliable for SSL learning. To address these challenges, we propose Modality Modulation Dual Consistency, dubbed MM-DC. Specifically, we design a modality all-in-one network to process data from all modalities, with learnable plug-in Modality Modulation Layers (MML) to gradually modulate features from different modalities into a modality-invariant feature space, enabling unified segmentation. Additionally, we propose a dual-consistency strategy that enforces consistency at both the image and feature levels, which eliminates the requirements for generative methods. Extensive experiments demonstrate that MM-DC outperforms other state-of-the-art methods on open-source datasets with 2- and 4-modalities. The code is available 1 .

关键词： image segmentation Codes Modulation Complex networks Semisupervised learning Reliability engineering Acoustics Speech processing Faces Biomedical imaging

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Improved Real-time Yoga Pose Estimation with GAN Augmentation

Improved Real-time Yoga Pose Estimation with GAN Augmentatio...

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Advances in signal Processing, Power, Communication, and Computing (ASPCC), ieee international conference on

作者： Kappa Dinesh Reddy Kalivarapu Sai Vasanth Abantika Jena Chinmayee Dora Sujata Chakravarty Department of CSE Centurion University of Technology & Management Bhubaneswar Odisha India Department of ECE Centurion University of Technology & Management Bhubaneswar Odisha India

ISBN: (数字)9798350355093

ISBN: (纸本)9798350355109

Yoga pose detection is challenging in computer vision due to variations in body postures and environmental conditions. Recent advancements in DL models have demonstrated encouraging achievements in this field. This study integrates Deep learning (DL) and Machine learning (ML) techniques to detect and monitor 20 Yoga postures through the real-time application. DL techniques like OpenPose, PoseNet, and PIFPAF are applied to the image and video dataset to obtain the keypoint features. These features are combined and provided to train various ML classifiers for Yoga posture detection tasks. Integrating AI augmentation technique Generative Adversarial Networks (GANs) plays a crucial role in improving the robustness and accuracy of the models. GANs are employed to generate synthetic data that mimics real-world variations in yoga poses and environments. By generating realistic variations in poses, backgrounds, lighting, and body shapes, GAN helped the models become more resilient to complex poses and diverse environmental conditions, enhancing their generalization capabilities. All the classifiers showed improvement with augmentation, whereas the Random Forest classifier performed the best in all parameters. Further, the model deployed with a webcam feed for estimating the Yoga pose by the yoga practitioner indicating accuracy level.

关键词： Accuracy Webcams Shape Computational modeling Pose estimation Streaming media signal processing Generative adversarial networks Real-time systems Synthetic data

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Research on image Classification of Chinese Medicine based on Improved Deep Learning technology

Research on Image Classification of Chinese Medicine Based o...

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2021 ieee international conference on Electronic Communications, internet of Things and Big Data, ICEIB 2021

作者： Gong, Daoqing Gan, Xinyan Gao, Xiang Ma, Yanni Huang, Jinchun Deng, Chuyan School of Public Health and Management Guangxi University of Chinese Medicine Nanning530200 China Guangxi College for Preschool Education Department of Special Education Nanning530022 China Guangxi University for Nationalities Nanning530006 China

ISBN: (纸本)9781665437554

Automatic classification of traditional Chinese medicine (TCM) images using deep learning has important application value as widely used in medical, teaching, and other fields. However, there is a lack of data collection of TCM images in domestic databases, and there are only a few studies on the classification of TCM images using deep learning techniques. To solve this problem, we collected 1000 TCM images of mango and almond leaves, both of which are difficult to distinguish. Comparison of this dataset with improved residual networks and deep neural networks such as CNN, VGG16, and VGG19 shows that the best results and the classification accuracies were over 95%. The deep neural network is ideal in two complex TCM image backgrounds and has some future application prospects. © 2021 ieee.

关键词： image classification

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Enhanced Diabetic Retinopathyanalysis: Unet-based Lesion Segmentation Coupled with Mobilenetv2 for Feature Extraction and Efficientnetb0 for Classification

Enhanced Diabetic Retinopathyanalysis: Unet-Based Lesion Seg...

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ieee international conference on Intelligent Techniques in Control, Optimization and signal Processing (INCOS)

作者： Sankara Narayanan S Sayan Kumar Bag Shivansh Singh Dept of Computing Technologies SRM Institute of Science & Technology Kattankulatur India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

In this research, we address the critical issue of precise Diabetic Retinopathy (DR) diagnosis, a condition of ten leading severe vision impairment or blindness in diabetic patients. Leveraging advanced deep learning models and innovative image processing techniques, our study focuses on accurate retinal image segmentation using a UNet model. This segmentation method delineates lesions and retinal structures effectively. Subsequently, Gabor filters are applied for intricate texture pattern extraction, indicative of diverse retinopathy stages. Integrating MobileNetV2 for feature extraction and EfficientNetB0 for multi-class classification significantly enhances the diagnostic accuracy. Our developed system exhibits a promising 91.2% test accuracy, showcasing its potential in DR diagnosis. While challenges related to varying severity levels persist, our robust framework lays the groundwork for future refinements. By amalgamating sophisticated image segmentation, feature extraction, and classification techniques, our system provides a solid foundation for accurate and timely DR assessment. With continuous enhancements, including the incorporation of more extensive and diverse datasets, our approach holds the promise to revolutionize DR diagnostics. The integration of cutting-edge technology into medical practices underscores the transformative impact of artificial intelligence in the realm of ophthalmology, promising improved patient outcomes.

关键词： image segmentation Diabetic retinopathy Visual impairment Feature extraction Retina Solids Gabor filters

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Visual-based Real-time Truck Scales Parking Boundary Crossing Detection

Visual-based Real-time Truck Scales Parking Boundary Crossin...

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Electronic Communications, internet of Things and Big Data (ICEIB), international conference on

作者： Mao-Yin Wang Hao-Yang Lee Sun-Yen Tan Lih-Jen Kau Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

The weighing of heavy trucks is used in many industries. To obtain an accurate weight of a weighing vehicle, the vehicle needs to be accurately parked and does not exceed the boundary of the truck scale. For this, a vision-based parking line crossing detection system is proposed in this article. The proposed system uses two or more cameras placed around the truck scale to monitor if the parking vehicle presses the boundary of the truck scale. After the user defines the parking area in the interface, the system automatically generates an electronic fence of the area for the decision boundary. The system then uses the Mixture of Gaussians (MOG2) background subtraction algorithm to detect the foreground. Thus, the decision boundary is analyzed to determine if the vehicle is parked over the line. To verify the performance and robustness of the proposed algorithm, the system was evaluated under sunny days, nighttime, and rainy days. The proposed system was effective in detecting vehicle parking line crossing in different lights. In addition, the precision of the system reached 97.7, 100, and 96.6% on sunny days, nighttime, and rainy days, respectively. The system can be easily deployed in edge devices and can effectively enhance the efficiency and correctness of the weighing process for heavy trucks.

关键词： Industries Presses Accuracy image edge detection Big Data Cameras Robustness

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