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Journal of Network Intelligence 2024年第1期9卷 142-159页

作者： Xiong, Jun Meng, Xiang-Long Chen, Zhao-Qi Wang, Chuan-Sheng Zhang, Fu-Quan Grau, Antoni Chen, Yang Huang, Jing-Wei School of Computer and Data Science Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China College of Electronic Engineering Shandong University of Science and Technology No. 579 Qianwangang Road Huangdao District Qingdao China College of Computer and Big Data Fuzhou University Fuzhou University Town No. 2 Wulongjiang North Road Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Autonomous Region of Catalonia Barcelona Spain Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China Fujian Province University No. 1 Campus New Village Longjiang Street Fuqing China School of Mechanical and Automotive Engineering Fujian University of Technology No. 33 Xuefu South Road University New District Fuzhou China

The electroencephalogram (EEG) serves as a significant tool in the realms of clinical medicine, cerebral investigation, and neurological disorders research. However, the EEG records we obtain are often easily contaminated by various artifacts, which can blur or distort the underlying EEG signals and make data interpretation difficult. Generally speaking, removing EEG artifacts is considered an essential step in brain signal analysis. Therefore, removing artifacts is crucial for obtaining accurate and reliable EEG signals for subsequent analysis. Recently, deep learning techniques have found widespread application across various domains for denoising tasks, including image denoising and EEG denoising. Many advanced algorithms have been developed in image denoising, which has achieved good results in enhancing low-quality images. Moreover, it has shown superior performance in EEG denoising. In contrast, few people have devoted themselves to studying EEG denoising, and existing convolutional neural network EEG denoising methods still have problems of overfitting and poor denoising effect in Electromyograph(EMG) and ElectroOculoGram(EOG) artifact removal. Therefore, this paper proposes a method called DWINet (De-artifacting with Image-based Network for EEG Signals) based on an image dehazing network DRHNet for removing artifacts from EEG signals. Specifically, our approach DWINet, addresses the de-artifacting issue in EEG signals by converting it as an image dehazing problem and utilizes the image dehazing capability of DRHNet to enhance the denoising performance of EEG signals. Experimental results demonstrate that the proposed method outperforms the compared algorithms in removing the ocular artifact in EEG signals and exhibits higher accuracy and robustness. © 2024, J. Network Intell. All rights reserved.

关键词： Image enhancement

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Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI

arXiv

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arXiv 2024年

作者： Wang, Zi Xiao, Min Zhou, Yirong Wang, Chengyan Wu, Naiming Li, Yi Gong, Yiwen Chang, Shufu Chen, Yinyin Zhu, Liuhong Zhou, Jianjun Cai, Congbo Wang, He Guo, Di Yang, Guang Qu, Xiaobo Department of Electronic Science Intelligent Medical Imaging R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China Institute of Artificial Intelligence Xiamen University China Human Phenome Institute Fudan University China Department of Imaging Xiamen Cardiovascular Hospital of Xiamen University School of Medicine Xiamen University China Department of Cardiovascular Medicine Heart Failure Center Ruijin Hospital Lu Wan Branch Shanghai Jiaotong University School of Medicine China Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University China Department of Radiology Zhongshan Hospital Fudan University Department of Medical Imaging Shanghai Medical School Shanghai Institute of Medical Imaging China Fujian Province Key Clinical Specialty Construction Project Medical Imaging Department Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence China School of Computer and Information Engineering Xiamen University of Technology China Department of Bioengineering and Imperial-X Imperial College London United Kingdom Department of Bioengineering Imperial College London United Kingdom

Dynamic magnetic resonance imaging (MRI) plays an indispensable role in cardiac diagnosis. To enable fast imaging, the k-space data can be undersampled but the image reconstruction poses a great challenge of high-dimensional processing. This challenge necessitates extensive training data in deep learning reconstruction methods. In this work, we propose a novel and efficient approach, leveraging a dimension-reduced separable learning scheme that can perform exceptionally well even with highly limited training data. We design this new approach by incorporating spatiotemporal priors into the development of a Deep Separable Spatiotemporal Learning network (DeepSSL), which unrolls an iteration process of a 2D spatiotemporal reconstruction model with both temporal low-rankness and spatial sparsity. Intermediate outputs can also be visualized to provide insights into the network behavior and enhance interpretability. Extensive results on cardiac cine datasets demonstrate that the proposed DeepSSL surpasses state-of-the-art methods both visually and quantitatively, while reducing the demand for training cases by up to 75%. Additionally, its preliminary adaptability to unseen cardiac patients has been verified through a blind reader study conducted by experienced radiologists and cardiologists. Furthermore, DeepSSL enhances the accuracy of the downstream task of cardiac segmentation and exhibits robustness in prospectively undersampled real-time cardiac MRI. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

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Image reconstruction from photoacoustic projections

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Photonics Insights 2024年第3期3卷 145-209页

作者： Chao Tian Kang Shen Wende Dong Fei Gao Kun Wang Jiao Li Songde Liu Ting Feng Chengbo Liu Changhui Li Meng Yang Sheng Wang Jie Tian Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefeiChina School of Engineering Science University of Science and Technology of ChinaHefeiChina Department of Anesthesiology the First Affiliated Hospital of USTCDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiChina Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing HefeiChina College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjingChina School of Information Science and Technology ShanghaiTech UniversityShanghaiChina Shanghai Clinical Research and Trial Center ShanghaiChina CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijingChina School of Precision Instruments and Optoelectronics Engineering Tianjin UniversityTianjinChina Academy for Engineering and Technology Fudan UniversityShanghaiChina Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhenChina Department of Biomedical Engineering College of Future TechnologyPeking UniversityBeijingChina National Biomedical Imaging Center Peking UniversityBeijingChina Departments of Ultrasound State Key Laboratory of Complex Severe and Rare DiseasesPeking Union Medical College HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina School of Engineering Medicine Beihang UniversityBeijingChina Key Laboratory of Big Data-Based Precision Medicine Beihang UniversityMinistry of Industry and Information TechnologyBeijingChina

Photoacoustic computed tomography(PACT)is a rapidly developing biomedical imaging modality and has attracted substantial attention in recent *** reconstruction from photoacoustic projections plays a critical role in image formation in *** we review six major classes of image reconstruction approaches developed in the past three decades,including delay and sum,filtered back projection,series expansion,time reversal,iterative reconstruction,and deep-learning-based *** principal ideas and implementations of the algorithms are summarized,and their reconstruction performances under different imaging scenarios are *** challenges,future directions,and perspectives for the development of image reconstruction algorithms in PACT are also *** review provides a self-contained reference guide for beginners and specialists in the photoacoustic community,to facilitate the development and application of novel photoacoustic image reconstruction algorithms.

关键词： optical imaging photoacoustic tomography image reconstruction deep learning

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Artificial Intelligence without Restriction Surpassing Human Intelligence with Probability One: Theoretical Insight into Secrets of the Brain with AI Twins of the Brain

arXiv

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arXiv 2024年

作者： Huang, Guang-Bin Westover, M. Brandon Tan, Eng-King Wang, Haibo Cui, Dongshun Ma, Wei-Ying Wang, Tiantong He, Qi Wei, Haikun Wang, Ning Tian, Qiyuan Lam, Kwok-Yan Yao, Xin Wong, Tien Yin School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Beth Israel Deaconess Medical Center Harvard Medical School Boston United States Department of Neurology National Neuroscience Institute Singapore Research Centre of Big Data and Artificial Intelligence for Medicine First Affiliated Hospital of Sun Yat-Sen University Guangzhou China Duke-NUS Medical School National University of Singapore Singapore Mind PointEye Singapore Institute for AI Industry Research Tsinghua University Beijing China College of Computing and Data Science Nanyang Technological University Singapore School of Biomedical Engineering Tsinghua University Beijing China Singapore AI Safety Institute Nanyang Technological University Singapore School of Data Science Lingnan University Hong Kong School of Computer Science University of Birmingham United Kingdom Singapore Eye Research Institute Singapore National Eye Centre Singapore Tsinghua Medicine Tsinghua University Beijing China

Artificial Intelligence (AI) has apparently become one of the most important techniques discovered by humans in history while the human brain is widely recognized as one of the most complex systems in the universe. One fundamental critical question which would affect human sustainability remains open: Will artificial intelligence (AI) evolve to surpass human intelligence in the future? This paper shows that in theory new AI twins with fresh cellular level of AI techniques for neuroscience could approximate the brain and its functioning systems (e.g. perception and cognition functions) with any expected small error and AI without restrictions could surpass human intelligence with probability one in the end. This paper indirectly proves the validity of the conjecture made by Frank Rosenblatt 70 years ago about the potential capabilities of AI, especially in the realm of artificial neural networks. This paper also gives the answer to the two widely discussed fundamental questions: 1) whether AI could have potentials of discovering new principles in nature;2) whether error backpropagation (BP) algorithm commonly and efficiently used in tuning parameters in AI applications is also adopted in the brain. Intelligence is just one of fortuitous but sophisticated creations of the nature which has not been fully discovered. Like mathematics and physics, with no restrictions artificial intelligence would lead to a new subject with its self-contained systems and principles. We anticipate that this paper opens new doors for 1) AI twins and other AI techniques to be used in cellular level of efficient neuroscience dynamic analysis, functioning analysis of the brain and brain illness solutions;2) new worldwide collaborative scheme for interdisciplinary teams concurrently working on and modelling different types of neurons and synapses and different level of functioning subsystems of the brain with AI techniques;3) development of low energy of AI techniques with the aid of fundament

关键词： Neurology

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Effect of respiratory microflorae colonization on short and long-time outcomes of respiratory syncytial virus infection in children: A scoping review

Pediatric Discovery

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Pediatric Discovery 2024年第4期2卷

作者： Lidan Gan Enmei Liu Yu Deng Department of Respiratory Medicine Children's Hospital of Chongqing Medical University Chongqing China Ministry of Education Key Laboratory of Child Development and Disorders Children's Medical Big Data Intelligent Application Center Department of Respiratory Medicine Chongqing China National Clinical Research Center for Child Health and Disorders Chongqing China

Respiratory syncytial virus (RSV) is an essential cause of lower respiratory tract infection in children under 2 years of age, especially under 6 months. In decades, studies have shown that the respiratory tract microflorae with RSV infection were related to disease severity and played a role in the development of recurrent wheezing, but the effect of respiratory microflorae on RSV infection are still underestimated. This study aims to conclude the effect of respiratory microflorae colonization on RSV infectious disease severity and recurrent wheezing and provide suggestions for future research directions from the perspective of respiratory tract florae. We conducted a scoping review. Studies were eligible if they reported on the effect of microflorae on RSV infectious diseases among children. We exacted the following information: title, publication time, first author's country, and article type. We finally included 33 articles in this scoping review. The number of studies rapidly increased since 2013 and the highest number of hospitalizations were reported in children <2 years. More than half (69.70%) were conducted in America and most studies are original studies (57.58%). The Review highlighted that the respiratory microflorae played an important role in RSV infectious disease severity and recurrent wheezing. We found that Streptococcus pneumoniae (***), Haemophilus influenza (HI), Moraxella catarrhalis (***), and Staphylococcus aureus (SA) were the dominant profiles in children with RSV infection. Understanding the respective role of respiratory microflorae on RSV infection and its mechanisms would improve prevention and treatment strategies from the perspective of microflorae.

关键词： childhood asthma disease severity microflorae recurrent wheezing respiratory syncytial virus

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An improved Chinese text multi-label classification method based on CNN

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Journal of Physics: Conference Series 2020年第1期1619卷

作者： Yuanxia Xin Zhi Zhang Collage of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430065 Hubei China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan 430065 Hubei China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan 430065 Hubei China

Text multi-label classification technology can accurately and quickly classify text information into related categories or topics, and help people quickly locate the required content in massive information resources, which is of great significance in application. As the traditional classification algorithm is faced with the problems of low classification accuracy due to the low correlation of data labels, unbalanced label data and few short text feature words, this paper firstly performs hierarchical pre-processing on label data to transform multi-label classification into hierarchical text multi-classification. At the same time, an improved multi-label classification algorithm Multi-label Convolutional Neural Networks (ML-CNN) is proposed. Based on the TensorFlow framework, a CNN model is designed and different training models are constructed for each level of label classification. According to the number of classification levels, the output of the upper level label is stitched to the original input tail as the next level of input. Experiments on the description information of 500,000 Chinese products with labels, show that the improved algorithm will significantly improve the classification accuracy and the accuracy of each level can reach more than 88%, which proves the feasibility and effectiveness of the algorithm.

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Preface

Advances in Intelligent Systems and Computing

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Advances in intelligent Systems and computing 2019年 834卷 ix页

作者： Pan, Jeng-Shyang Lin, Jerry Chun-Wei Sui, Bixia Tseng, Shih-Pang Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology FuzhouFujian China Department of Computing Mathematics and Physics Western Norway University of Applied Sciences Bergen Norway Changzhou College of Information Technology ChangzhouJiangsu China College of Smart Living and Management Tajen University Pingtung Taiwan

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Circulating proteins and peripheral artery disease risk: observational and Mendelian randomization analyses

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European Heart Journal Open 2023年第3期3卷 oead056页

作者： Yuan, Shuai Titova, Olga E. Zhang, Ke Chen, Jie Li, Xue Klarin, Derek Åkesson, Agneta Damrauer, Scott M. Larsson, Susanna C. Unit of Cardiovascular and Nutritional Epidemiology Institute of Environmental Medicine Karolinska Institutet Nobels väg 13 Solna Stockholm 171 65 Sweden Unit of Medical Epidemiology Department of Surgical Sciences Uppsala University Uppsala Sweden Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province School of Life Sciences Westlake University Hangzhou China Westlake Intelligent Biomarker Discovery Lab Westlake Laboratory of Life Sciences and Biomedicine Hangzhou China Department of Big Data in Health Science School of Public Health Center of Clinical Big Data and Analytics The Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China VA Palo Alto Healthcare System 4951 Arroyo Rd Livermore 94550 CA United States Department of Surgery Stanford University School of Medicine Stanford University Palo Alto CA United States Corporal Michael J. Crescenz VA Medical Center Philadelphia PA United States Department of Surgery Department of Genetics Perelman School of Medicine University of Pennsylvania Philadelphia PA United States

Aims We conducted observational and Mendelian randomization (MR) analyses to explore the associations between blood proteins and risk of peripheral artery disease (PAD).;Methods and results The observational cohort analyses included data on 257 proteins estimated in fasting blood samples from 12 136 Swedish adults aged 55–94 years who were followed up for incident PAD via the Swedish Patient Register. Mendelian randomization analyses were undertaken using cis-genetic variants strongly associated with the proteins as instrumental variables and genetic association summary statistic data for PAD from the FinnGen study (11 924 cases and 288 638 controls) and the Million Veteran Program (31 307 cases and 211 753 controls). The observational analysis, including 86 individuals diagnosed with incident PAD during a median follow-up of 6.6-year, identified 13 proteins [trefoil factor two, matrix metalloproteinase-12 (MMP-12), growth differentiation factor 15, V-set and immunoglobulin domain-containing protein two, N-terminal prohormone brain natriuretic peptide, renin, natriuretic peptides B, phosphoprotein associated with glycosphingolipid-enriched microdomains one, C-C motif chemokine 15, P-selectin, urokinase plasminogen activator surface receptor, angiopoietin-2, and C-type lectin domain family five member A] associated with the risk of PAD after multiple testing correction. Mendelian randomization analysis found associations of T-cell surface glycoprotein CD4, MMP-12, secretoglobin family 3A member 2, and ADM with PAD risk. The observational and MR associations for T-cell surface glycoprotein CD4 and MMP-12 were in opposite directions. Conclusion This study identified many circulating proteins in relation to the development of incident PAD. Future studies are needed to verify our findings and assess the predictive and therapeutic values of these proteins in PAD. © The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.

关键词： Cohort Mendelian randomization Protein Peripheral artery disease

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Cascade control for supply air temperature in a variable air volume system

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IOP Conference Series: Earth and Environmental Science 2019年第1期238卷

作者： Junhua Zhuang Yimin Chen Jie Wu School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing China Department of Civil Architectural and Environmental Engineering Drexel University Philadelphia PA 19104 USA Beijing ZXT M&E Engineering Consultant Co. Ltd

Most supply air temperature in variable air volume systems is typically set at 55F(13 °C) that is design value in these systems. However, sometimes the load of cooling varies greatly in different zones, and it means the indoor temperature of low-load zones is lower than comfortable value, even if the supply air damper is at the minimum value that is not closed for minimum outside air requirement. Meanwhile, in transition season, the indoor temperature is also lower due to low outdoor temperature. These factors provided an opportunity to apply higher supply air temperature to reduce air handling unit (AHU) cooling load and box reheating. In this paper, a cascade control method is used to raise the supply air temperature. The external control loop controls the setting value of supply air temperature with the minimum supply air volume as the control target. And the internal control loop controls the supply air temperature by regulating valve. The cascade structure can solve the problem that the time constant of the system is too large. The simulation results show that the quality of control is good and the reheating can be cancelled.

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Comparison of UAV-based multispectral sensors for detection of Solenopsis invicta Nests

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IOP Conference Series: Earth and Environmental Science 2020年第1期569卷

作者： Yuejun Song Feng Chen Kaitao Liao Jiangxi Institute of Soil and Water Conservation Nanchang 330029 China Jiangxi Key Laboratory of Soil Erosion and Prevention Nanchang 330029 China College of Computer and Information Engineering Xiamen University of Technology Xiamen 361024 China Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of Technology Xiamen 361024 China Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Informatics Xiamen University Xiamen 361005 China

The invasive red imported fire ant (Solenopsis invicta Buren) (Hymenoptera: Formicidae) has been continuing to expand its range in China, resulting in adverse ecological impacts to where it has invaded. As such, the reliable detection of Solenopsis invicta Buren nests is needed to effectively manage the species. In this paper, the spectral features of the Solenopsis invicta Buren nests were demonstrated using spectra samples obtained by ASD FieldSpec 4 during field surveys in Shanggao County, Jiangxi Province. Furthermore, channel reflectance estimations for three unmanned aerial vehicle (UAV)-based multispectral sensors were simulated correspondingly. Based on the channel reflectance from individual sensors, the detection of the Solenopsis invicta Buren nests was investigated. Generally, between-class differences in reflectance were relatively significant in both Green channel and Red channel, which suggests the importance of these two channels for the detection of Solenopsis invicta Buren nest. Minor between-sensor differences observed in both Green channel and Red channel suggest that detection results obtained from UAV-based multispectral observations could be comparable among the three sensors, considering only the spectral features. There are significant differences in reflectance between the soil of Solenopsis invicta Buren nests and ordinary soil over the longwave region of near infrared (NIR). It suggests the spectral measurement over the longwave region of NIR could be more useful in distinguishing the soil of Solenopsis invicta Buren nests from ordinary soil. Nevertheless, the multispectral sensors provided with channels covering visible and the shortwave region of NIR, possibly do not meet requirements completely for application. An effective method for detecting Solenopsis invicta Buren nests using UAV-based multispectral sensors is currently being evaluated.

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