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Passively Mode-Locked Er-Doped Fiber Laser with Single and Double Wavelength Pulses Based on Germanene Saturable Absorber

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SSRN 2023年

作者： Ding, Jiashuai Liu, Xueyao Li, Tuo Zou, Xiaofeng Yu, Yanfeng Xu, Nannan Sahng, Xinxin Lu, Hua Cheng, Chen Huang, Pu Zhang, Huanian Li, Dengwang Shandong Key Laboratory of Medical Physics and Image Processing Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine School of Physics and Electronics Shandong Normal University Shandong Jinan250358 China Shandong Yunhai Guochuang Cloud Computing Equipment Industry Innovation Co. Ltd. Jinan China School of Physics and Optoelectronic Engineering Shandong University of Technology Shandong Zibo255049 China

In this paper, we focus on the single crystal material germanium (Ge), which is fabricated into saturable absorbers (SA) employing liquid phase exfoliation, and validate it with an erbium-doped fiber laser (EDFL). The Ge SA was obtained with a modulation depth of 9.8 % and a saturation intensity of 11.02 MW/cm2. The single wavelength mode-locked pulse with a minimum pulse width of 847 fs was obtained at a cavity length of 10.5 m. In addition, at a cavity length of 106 m, a dual wavelength mode-locked phenomenon was obtained in which the central wavelengths were located at 1559.20 nm and 1561.31 nm. The experimental results show that Ge nanosheets in an EDFL provide a strong basis for the development of nonlinear optics and have a wide range of applications in the field of pulsed fiber lasers. © 2023, The Authors. All rights reserved.

关键词： Saturable absorbers

An Artificial Intelligence-Based Ecological Index for Prognostic Evaluation of Colorectal Cancer

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SSRN 2022年

作者： Chen, Qicong Cai, Ming Fan, Xinjuan Liu, Wenbin Fang, Gang Yao, Su Xu, Yao Li, Qian Zhao, Yingnan Zhao, Ke Liu, Zaiyi Chen, Zhihua Institute of Computing Science and Technology Guangzhou University Guangzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou China Department of Radiology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou China Department of Pathology The Sixth Affiliated Hospital of Sun Yat-sen University Guangzhou China Department of Pathology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou China Guangdong Cardiovascular Institute Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou China

Background and Objective: In the tumor microenvironment (TME), the active interaction between tumor cells and immune cells is crucial for predicting the prognosis of colorectal cancer. This study proposed a Morisita-Horn ecological index to quantify the spatial associations between tumor cells and immune cells in the TME by artificial intelligence (AI)-based method from immunohistochemistry (IHC)-stained whole-slide images (WSIs) of colorectal cancer (CRC) *** and methods: We used deep learning technology and traditional computer segmentation methods to segment the tumor nuclei, immune nuclei, and stroma nuclei in tumor regions in IHC-stained WSIs. The Morisita-Horn ecological index (Mor-index) was used to evaluate the spatial associations between the tumor cells and immune cells in the TME of CRC patients. A discovery cohort (N = 432) and validation cohort (N = 137) were used to evaluate the prognostic value of Mor-index for overall survival (OS).Results: Experiments were conducted on two data sets of 569 patients to demonstrate the efficacy of the method. Compared to other studies, our method is not only superior to the QuPath tool but also produces better segmentation results with an accuracy of 0.85. Mor-index was quantified automatically by our method. Survival analysis indicated that the higher Mor-index correlated with better OS in the discovery cohorts (HR for high vs. low 0.49, 95% CI 0.27–0.77, P = 0.0014) and validation cohort (0.21, 0.10–0.46, Conclusion: This study provided a novel AI-based approach to segmenting various nuclei in the TME. The Mor-index can reflect the immune status of CRC patients and is associated with favorable survival. Thus, Mor-index can potentially make a significant role in aiding clinical prognosis and decision-making. © 2022, The Authors. All rights reserved.

关键词： Tumors

TEVA: Training-Efficient and Verifiable Aggregation for Federated Learning for Consumer Electronics in Industry 5.0

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IEEE Transactions on Consumer Electronics 2024年

作者： Xia, Yuanjun Liu, Yining Chen, Jingxue Liang, Yangfan Khan, Fazlullah Alturki, Ryan Wang, Xiaopei Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software School of Computer and Information Security Guilin541004 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325027 China University of Technology School of Mathematical and Physical Sciences SydneyNSW2007 Australia Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems The Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing314001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China Umm AI-Qura University Makkah Department of Software Engineering College of Computing Saudi Arabia Riverside Department of Computer Science and Engineering University of California CA92521 United States

Federated learning (FL) has been widely used for privacy-preserving model updates in Industry 5.0, facilitated by 6G networks. Despite FL's privacy-preserving advantages, it remains vulnerable to attacks where adversaries can infer private data from local models or manipulate the central server (CS) to deliver falsified global models. Current privacy-preserving approaches, primarily based on the FedAvg algorithm, fail to optimize training efficiency for non-independent and identically distributed (non-IID) data. This article proposes training-efficient and verifiable aggregation (TEVA) for FL to resolve these issues. This scheme combines threshold Paillier homomorphic encryption (TPHE), verifiable aggregation, and an optimized double momentum update mechanism (OdMum). TEVA not only leverages TPHE to protect the privacy of local models but also ensures the integrity of the global model through a verifiable aggregation mechanism. Additionally, TEVA integrates the OdMum algorithm to effectively address the challenges posed by non-IID data, promoting rapid model convergence and significantly enhancing overall training efficiency. Security analysis indicates that TEVA meets the requirements for privacy protection. Extensive experimental results demonstrate that TEVA can accelerate model convergence while incurring lower computational and communication overheads. © 1975-2011 IEEE.

关键词： Differential privacy

Evidence-Aware Multi-Modal Data Fusion and its Application to Total Knee Replacement Prediction

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Evidence-Aware Multi-Modal Data Fusion and its Application t...

Proceedings of the Digital image computing: Technqiues and Applications (DICTA)

作者： Xinwen Liu Jing Wang S. Kevin Zhou Craig Engstrom Shekhar S. Chandra School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia The Commonwealth Scientific and Industrial Research Organisation Canberra Australia Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) School of Biomedical Engineering & Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China School of Human Movement and Nutrition Sciences The University of Queensland Brisbane Australia

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

Deep neural networks have been widely studied to predict a medical condition, such as total knee replacement (TKR). It has shown that data of different modalities, such as imaging data, clinical variables, and demographic information, provide complementary information and thus can improve the prediction accuracy together. However, the data sources of various modalities may not always be of high quality, and each modality may have only partial information of medical condition. Thus, predictions from different modalities can be in conflict, and the final prediction may fail in the presence of such a conflict. Therefore, it is important to account for the reliability of each source data and the prediction output when making a final decision. In this paper, we propose an evidence-aware multimodal data fusion framework based on the Dempster-Shafer theory (DST). The backbone models contain an image branch, a non-image branch and a fusion branch. For each branch, there is an evidence network that takes the extracted features as input and outputs an evidence score, which is designed to represent the reliability of the output from the current branch. The output probabilities along with the evidence scores from multiple branches are combined with the Dempster's combination rule to make a final prediction. Experimental results on the public OA initiative (OAI) dataset for the TKR prediction task show that the proposed method has better performance by accounting for conflicts from various modalities.

关键词： medical conditions Soft sensors Digital images Data integration Imaging Artificial neural networks Reliability theory Feature extraction Reliability engineering Knee replacement

MedSteGAN: medical Embedding-Optimized Steganography with Improved GAN

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SSRN 2023年

作者： Peng, Yuxiang Fu, Chong Zheng, Yu Tian, Yunjia Cao, Guixing Chen, Junxin School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang110819 China Department of Information Engineering The Chinese University of Hong Kong Ma Liu Shui Shatin New Territories Hong Kong Institute of Telecommunication Satellite China Academy of Space Technology Beijing100094 China School of Software Dalian University of Technology Liaoning Dalian116621 China

medical diagnosing systems contain sensitive information, especially the resulting certificate of diagnosis that involves the patient’s privacy, required to be protected carefully. An effective way is to secretly embed privacy-sensitive information into medical-generated images, eventually being viewed as arbitrary images of nobody/nothing in public. The current research line of medical steganography targets simple data-hiding methods, yet remains barren in optimal embedding and stronger robustness. To fill this gap, we propose an end-to-end steganographic framework for medical images. Our idea is to integrate medical-image adaptive steganography with a generative adversarial network (GAN), named MedSteGAN. To improve both robustness, we design an optimized U-shape gen- erator that extracts features more efficiently utilizing larger kernel convolutions and the ReLU6 function. Also, deconvolutions are replaced by upsampling and convolution layers to avoid the checkerboard effect. To enhance security and image quality, we employ visual quality loss and mean squared error (MSE) loss based on the multi-scale structural similarity index metric (MS- SSIM) and the high-pass filters, respectively. Extensive experiments demonstrate that MedSteGAN outperforms state-of-the-art steganographic schemes in terms of robustness and image quality. © 2023, The Authors. All rights reserved.

关键词： image quality

Processing framework and the fast volume rendering algorithms for out-of-core medical data

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Ruan Jian Xue Bao/Journal of Software 2008年第12期19卷 3237-3248页

作者： Xue, Jian Tian, Jie Dai, Ya-Kang Chen, Jian College of Computing and Communication Engineering Graduate University The Chinese Academy of Sciences Beijing 100049 China Medical Image Processing Group Institute of Automation The Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Complex Systems and Intelligence Science Beijing 100190 China

This paper designs and implements an algorithm framework for the out-of-core medical data processing and analyzing and integrates it into MITK (medical imaging toolkit), an algorithm toolkit for medical image processing and analyzing accomplished by the group. With the help of this, a processing platform for the out-of-core medical data is set up and fast out-of-core volume rendering algorithms based on volume ray casting and 3D texture are studied in this paper. A semi-adaptive partitioning method is proposed to divide original data sets into sub-blocks and get a better partitioning result without influencing the partitioning speed. Furthermore, the graphics hardware is also used to accelerate the rendering process. The experimental results indicate that the new framework and algorithms are effective and efficient for the processing and visualization of the out-of-core medical data sets. © 2008 by Journal of Software.

关键词： Volume rendering

Msnoa-Catboost: A Novel Pan-Cancer Diagnosis Model with Feature Subset Selection and Hyperparameter Optimization

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SSRN 2024年

作者： Xiao, Tianyun Zhang, Zichen Kong, Shanshan Liu, Fengchun Yang, Aimin Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Hebei Tangshan063210 China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and Technology Hebei Tangshan063210 China College of Science North China University of Science and Technology Hebei Tangshan063210 China Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and Technology Hebei Tangshan China Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and Technology Hebei Tangshan China

Cancer, as a global epidemic disease, remains a significant threat to human life and health. Prevention and early diagnosis are widely recognized as effective ways to tackle cancer. With the development of big data technology, many data-driven cancer diagnosis models have been proposed. However, the accuracy and generalization ability of existing models often fail to meet practical application needs. In order to address these challenges, this paper proposes a novel and improved protocol for high-precision pan-cancer diagnosis based on five publicly available cancer datasets from the UCI database and Kaggle, covering different cancer types such as breast, lung, and glioma. To achieve feature subset selection and hyperparameter optimization, we first introduce the nutcracker optimization algorithm (NOA), and improve it by using a multi-strategy method (MSNOA) to enhance optimization performance and efficiency. In the comparative experiment phase, we compare several popular machine learning models and identify MSNOA-CatBoost as the proposed model. The experimental results show that MSNOA-CatBoost improves the accuracy of the five datasets by an average of 9.12% compared to the baseline optimal model, and outperforms existing models in terms of performance. In summary, this study presents a promising approach to achieving high-precision pan-cancer diagnosis by leveraging the power of big data and advanced machine learning techniques. The proposed MSNOA-CatBoost model shows remarkable performance improvement over existing models, and has the potential to be applied in clinical practice for cancer diagnosis and treatment planning. © 2024, The Authors. All rights reserved.

关键词： Optimization

Dual-Scale Enhanced and Cross-Generative Consistency Learning for Semi-Supervised medical image Segmentation

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SSRN 2024年

作者： Gu, Yunqi Zhou, Tao Zhang, Yizhe Zhou, Yi He, Kelei Gong, Chen Zhu, Huafu PCA Lab The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Information Science and Technology Nanjing210094 China School of Computer Science and Engineering Southeast University Nanjing211189 China Medical School Nanjing University National Institute of Healthcare Data Science Nanjing210023 China Institute of High Performance Computing A*STAR Singapore

medical image segmentation plays a crucial role in computer-aided diagnosis. However, existing methods heavily rely on fully supervised training, which requires a large amount of labeled data with time-consuming pixel-wise annotations. Moreover, accurately segmenting lesions poses challenges due to variations in shape, size, and location. To address these issues, we propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image Segmentation (DEC-Seg). First, we propose a Cross-level Feature Aggregation (CFA) module that integrates cross-level adjacent layers to enhance the feature representation ability across different resolutions. To address scale variation, we present a scale-enhanced consistency constraint, which ensures consistency in the segmentation maps generated from the same input image at different scales. This constraint helps handle variations in lesion sizes and improves the robustness of the model. Furthermore, we propose a cross-generative consistency scheme, in which the original and perturbed images can be reconstructed using cross-segmentation maps. This consistency constraint allows us to mine effective feature representations and boost the segmentation performance. To produce more accurate segmentation maps, we propose a Dual-scale Complementary Fusion (DCF) module that integrates features from two scale-specific decoders operating at different scales. Extensive experimental results on polyp segmentation from colonoscopy images, skin lesion segmentation from dermoscopic images, and Brain glioma segmentation from MRI images demonstrate the effectiveness of our DEC-Seg against other state-of-the-art semi-supervised segmentation approaches. © 2024, The Authors. All rights reserved.

关键词： Computer aided diagnosis

CS-Net: A Two-Step Epithelium Tissue Segmentation Regression Network with CS-Gate Attention on Histology images

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SSRN 2022年

作者： Gong, Zhengze Pan, Xipeng Han, Chu Qiu, Bingjiang Zhao, Bingchao Liu, Yu Chen, Xinyi Liu, Wenbin Chen, Zhihua Lu, Cheng Liu, Zaiyi Fang, Gang Institute of Computing Science and Technology Guangzhou University Guangzhou510006 China Department of Radiology Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China Guangdong Cardiovascular Institute Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China

Background and Objective: The segmentation of oropharyngeal epithelium tissue within Haematoxylin&Eosin(HE)-stained pathological images is of great clinical significance for the pathological analysis and diagnosis. However, due to the imperfection of staining, staining fainting, or variations between staining methods in different labs, the contrast between epithelium and stroma may not be obvious in the HE-stained *** and methods: To address these challenges, we propose a two-step method, including a stain-style transfer step and a threshold segmentation step. In the first step, we propose a stain-style transfer network named CS-Net, together with our proposed attention module named CS-Gate, to convert the HE-stained image into an Immunohistochemistry (IHC)-stained image, aiming to enhance the appearance of epithelial tissue. In the second step, we perform threshold segmentation on the synthetic IHC-stained image to finally obtain a binary mask of oropharyngeal epithelium ***: We conduct experiments on Oropharyngeal Cancer Tissue-Microarray image dataset, and the results show that CS-Net can generate a more precise binary mask than UNet-based segmentation networks(achieving an accuracy of 92.48%), while restoring colors and details better than general GAN-based networks (about 10% higher in Structural Similarity). Then we use Breast Cancer Cases (BCSS) public dataset as external validation, and find out that CS-Net has a stronger generalization capability. The code of CS-Net is available at https://***/frankie98kung/CS-Net. © 2022, The Authors. All rights reserved.

关键词： Tissue