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Harmonized-Multinational qEEG norms (HarMNqEEG)

NEUROimage 2022年 256卷 119190-119190页

作者： Li, Min Wang, Ying Lopez-Naranjo, Carlos Hu, Shiang Reyes, Ronaldo Cesar Garcia Paz-Linares, Deirel Areces-Gonzalez, Ariosky Hamid, Aini Ismafairus Abd Evans, Alan C. Savostyanov, Alexander N. Calzada-Reyes, Ana Villringer, Arno Tobon-Quintero, Carlos A. Garcia-Agustin, Daysi Yao, Dezhong Dong, Li Aubert-Vazquez, Eduardo Reza, Faruque Razzaq, Fuleah Abdul Omar, Hazim Abdullah, Jafri Malin Galler, Janina R. Ochoa-Gomez, John F. Prichep, Leslie S. Galan-Garcia, Lidice Morales-Chacon, Lilia Valdes-Sosa, Mitchell J. Trondle, Marius Zulkifly, Mohd Faizal Mohd Rahman, Muhammad Riddha Bin Abdul Milakhina, Natalya S. Langer, Nicolas Rudych, Pavel Koenig, Thomas Virues-Alba, Trinidad A. Lei, Xu Bringas-Vega, Maria L. Bosch-Bayard, Jorge F. Valdes-Sosa, Pedro Antonio [a]The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China [b]Cuban Center for Neurocience La Habana Cuba [c]McGill Centre for Integrative Neuroscience Ludmer Centre for Neuroinformatics and Mental Health Montreal Neurological Institute Canada [d]Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [e]Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [f]Hospital Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [g]Humanitarian Institute Novosibirsk State University Novosibirsk 630090 Russia [h]Laboratory of Psychophysiology of Individual Differences Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine Novosibirsk 630117 Russia [i]Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences Novosibirsk 630090 Russia [j]University of Pinar del Río “Hermanos Saiz Montes de Oca” Pinar del Río Cuba [k]Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany [l]Department of Cognitive Neurology University Hospital Leipzig Leipzig Germany [m]Center for Stroke Research Charité-Universitätsmedizin Berlin Berlin Germany [n]Grupo Neuropsicología y Conducta - GRUNECO Faculty of Medicine Universidad de Antioquia Colombia [o]Research Department Institución Prestadora de Servicios de Salud IPS Universitaria Colombia [p]The Cuban center aging longevity and health Havana Cuba [q]Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu 2019RU035 China [r]School of Electrical Engineering Zhengzhou University Zhengzhou 4500

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects " and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

关键词： Quantitative EEG EEG cross-spectrum Riemannian geometry Batch effects Z-score Harmonization Malnutrition Covid induced brain dysfunction Developmental Brain Chart

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medical entity recognition of Esophageal Carcinoma based on word clustering

Medical entity recognition of Esophageal Carcinoma based on ...

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International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Rui Wang Jinfeng Zhao Lizhi Peng Bo Yang Lin Wang Baosheng Li Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan China Shangdong Medical Imaging and Radiotherapy Engineering Center Jinan P.R. China

ISBN: (纸本)9781728105529

Electronic medical Records (EMRs) is the core of medical information system in hospital. EMRs arises from the medical institution, and large amount of clinical data generated every day. Due to the EMRs contains many medical entities and clinical information of the patient, by analyzing and mining the texts data, medical knowledge which closely related to patients or certain diseases can be obtained. In this paper, we ues the EMRs of patients with Esophageal Carcinoma(EC), which include the clinical symptoms of the patients, tests they have undergone, results of the examinations, and the diagnosis and treatment plan. In this paper, word vector training is carried out for large-scale electronic medical record corpus by means of the skip model of word2vec deep learning tool. Then applying the features data to the k-means clustering algorithm to identifying medical entities about EC by word clustering. As the first step of medical knowledge mining, medical entity recognition is the premise of extracting the semantic relationship implied in medical text and structuring EMRs.

关键词： medical diagnostic imaging Training Clustering algorithms Semantics Artificial neural networks

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A content-based image retrieval scheme for identifying lung nodule malignancy levels 29

A content-based image retrieval scheme for identifying lung ...

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第29届中国控制与决策会议

作者： Guohui Wei He Ma Wei Qian Xinzhuo Zhao Sino-Dutch Biomedical and Information Engineering School Hunnan CampusNortheastern University Key Laboratory of Medical Image Computing Ministry of Education College of Engineering University of Texas at El Paso

ISBN: (纸本)9781509046584

Discriminating the lung nodules benign or malignant is an important task for computer aided diagnosis of lung *** malignancy of the nodules is divided into five levels in Lung image Database Consortium(LIDC) *** this study,a new content-based image retrieval(CBIR) scheme is proposed for classification of the lung nodules with different ratings.A lung nodule dataset is assembled from LIDC lung CT *** nodule density dependent features are calculated to depict each *** each queried nodule,a CBIR scheme is used to search for ten most similar reference nodules.A malignancy probability is computed to predict the malignancy of the lung *** results show that accuracy and the area under the curve(AUC) are 0.6655 and 0.6901 when classifying nodules moderately suspicious for cancer(rating 4) and highly suspicious(rating 5).The ACC and AUC are 0.9231 and 0.8659 when testing our scheme on differentiating benign and malignant cases.

关键词： content-based image retrieval computer-aided diagnosis lung nodules classification heterogeneity lesion density

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Sparse multi-kernel based multi-task learning for joint prediction of clinical scores and biomarker identification in Alzheimer’s disease 20th

Sparse multi-kernel based multi-task learning for joint pred...

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20th International Conference on medical image computing and Computer-Assisted Intervention, MICCAI 2017

作者： Cao, Peng Liu, Xiaoli Yang, Jinzhu Zhao, Dazhe Zaiane, Osmar Key Laboratory of Medical Image Computing of Ministry of Education College of Computer Science and Engineering Northeastern University Shenyang Shi China University of Alberta Edmonton Canada

ISBN: (纸本)9783319661780

Machine learning methods have been used to predict the clinical scores and identify the image biomarkers from individual MRI scans. Recently, the multi-task learning (MTL) with sparsity-inducing norm have been widely studied to investigate the prediction power of neuroimaging measures by incorporating inherent correlations among multiple clinical cognitive measures. However, most of the existing MTL algorithms are formulated linear sparse models, in which the response (e.g., cognitive score) is a linear function of predictors (e.g., neuroimaging measures). To exploit the nonlinear relationship between the neuroimaging measures and cognitive measures, we consider that tasks to be learned share a common subset of features in the kernel space as well as the kernel functions. Specifically, we propose a multi-kernel based multi-task learning with a mixed sparsity-inducing norm to better capture the complex relationship between the cognitive scores and the neuroimaging measures. The formation can be efficiently solved by mirror-descent optimization. Experiments on the Alzheimers Disease Neuroimaging Initiative (ADNI) database showed that the proposed algorithm achieved better prediction performance than state-of-the-art linear based methods both on single MRI and multiple modalities. © Springer International Publishing AG 2017.

关键词： Forecasting

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Detection of Late Blight in Potato Leaves Based on Multi-Feature and SVM Classifier

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Journal of Physics: Conference Series 2020年第1期1518卷

作者： Wenjie Liu Yongjun Zhang Haisheng Fan Yongjie Zou Yongbin Qin Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province College of Computer Science and Technology Guizhou University Guiyang China ZHUHAI ORBITA AEROSPACE SCIENCE&TECHNOLOGY Co. LTD. Oribita Tech Park 1 BaiSha Road TangJia DongAn ZhuHai China College of Computer Science and Technology Guizhou University

Due to the influence of germs and viruses, plants often show various symptoms of diseases and insect pests during the growth process, which leads to a large economic loss of fruit farmers. It also brings a certain economic loss to our society, so prevent earlier and advise growers about plant diseases and insect pests have important value and significance. In this case, this paper proposes a detection method which is based on the combination of HOG, LBP and CSS features with Support Vector Machine (SVM) classifier. This method extracts the histogram of oriented gradients, texture, and color self-similar features of potato leaves, and then training samples with SVM classifier to detect late blight as early as possible in the early stages of potato growth. In addition, this paper proposes a method to increase virtual samples, that is, generating symmetrical samples according to the original samples. Due to the limitation of the number of collected samples, increasing symmetrical samples can expand the diversity of samples. The results show that this method can obtain a detection rate of 92.7%, and has better detection and recognition performance in practical application.

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Finite-time Switching Synchronization of Four Real Chaotic Systems with Combination Mode

Finite-time Switching Synchronization of Four Real Chaotic S...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Weiqi Qin Yanfeng Wang Tianze Li Ming Guo Xiangyong Chen Xinru Tong Affiliated Hospital of Shandong Medical College Linyi P. R. China School of Engineering Huzhou University Huzhou P. R. China School of Automation and Electrical Engineering Linyi University Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi P. R. China College of Mathematics and Systems Science Xinjiang University Urumqi P. R. China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

This paper mainly studies the problem of finite-time switching synchronization for four real chaotic systems with combination mode. Firstly, we analyze multi-switching synchronization behavior in signal switching transmission, and the definition of finite-time combination synchronization is given by choosing the preset switching rules. And then, a novel finite time controller is designed to realize fast synchronization, and the sufficient conditions are given. Finally, numerical examples verify the effectiveness of the proposed control scheme.

关键词： Synchronization Switching systems Chaotic communication Control system synthesis

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Parameter-transferred wasserstein generative adversarial network (PT-WGAN) for low-dose PET image denoising

arXiv

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arXiv 2019年

作者： Gong, Yu Shan, Hongming Teng, Yueyang Tu, Ning Li, Ming Liang, Guodong Wang, Ge Wang, Shanshan College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Key Laboratory of Intelligent Computing in Medical Images Ministry of Education Shenyang110169 China Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States PET-CT/MRI Center Molecular Imaging Center Wuhan University Renmin Hospital Wuhan430060 China Neusoft Medical Systems Co. Ltd Shenyang110167 China

Due to the widespread use of positron emission tomography (PET) in clinical practice, the potential risk of PET-associated radiation dose to patients needs to be minimized. However, with the reduction in the radiation dose, the resultant images may suffer from noise and artifacts that compromise diagnostic performance. In this paper, we propose a parameter-transferred Wasserstein generative adversarial network (PT-WGAN) for lowdose PET image denoising. The contributions of this paper are twofold: i) a PT-WGAN framework is designed to denoise lowdose PET images without compromising structural details, and ii) a task-specific initialization based on transfer learning is developed to train PT-WGAN using trainable parameters transferred from a pretrained model, which significantly improves the training efficiency of PT-WGAN. The experimental results on clinical data show that the proposed network can suppress image noise more effectively while preserving better image fidelity than recently published state-of-the-art methods. We make our code available at https://***/90n9-yu/PT-WGAN. Copyright © 2019, The Authors. All rights reserved.

关键词： Positron emission tomography

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Modeling and hopf bifurcation analysis of benign worms with quarantine strategy 9th

Modeling and hopf bifurcation analysis of benign worms with ...

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9th International Symposium on Cyberspace Safety and Security, CSS 2017

作者： Yao, Yu Fu, Qiang Sheng, Chuan Yang, Wei Key Laboratory of Medical Image Computing of Ministry of Education Northeastern University Shenyang110004 China College of Compute Science and Engineering Northeastern University Shenyang110819 China Software College Northeastern University Shenyang110819 China

ISBN: (纸本)9783319694702

Since the Morris worm occurred in 1988, malwares have threatened the network persistently. With rapid development of the Internet, network security issues become increasingly serious. Recently, the benign worms become a new active countermeasure to deal with the worm threat. In this paper, we propose a compositive-hybrid benign worm propagation model with quarantine strategy. Usually, quarantine strategy will lead to a time delay, and the worm propagation system will be unstable and out of control with the time delay increases. Then the existence condition and the stability of the positive equilibrium are derived. Through our derivation and analysis, the threshold s0 of Hopf bifurcation is obtained. The system will be stable when time delay s\s0. In addition, numerical experiments are performed and the effect of benign worms is displayed by comparing with the model that do not include benign worms. Furthermore, simulation experiments are carried out to verify our conclusions.. © 2017, Springer International Publishing AG.

关键词： Time delay

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Walk-steered convolution for graph classification

arXiv

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arXiv 2018年

作者： Jiang, Jiatao Xu, Chunyan Cui, Zhen Zhang, Tong Zheng, Wenming Yang, Jian Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of the Ministry of Education School of Computer Science and Engineering University of Science and Technology Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering University of Science and Technology Nanjing210094 China Key Laboratory of Child Development and Learning Science of the Ministry of Education School of Biological Science and Medical Engineering Southeast University Nanjing210096 China

Graph classification is a fundamental but challenging issue for numerous real-world applications. Despite recent great progress in image/video classification, convolutional neural networks (CNNs) cannot yet cater to graphs well because of graphical non-Euclidean topology. In this work, we propose a walk-steered convolutional (WSC) network to assemble the essential success of standard convolutional neural networks as well as the powerful representation ability of random walk. Instead of deterministic neighbor searching used in previous graphical CNNs, we construct multi-scale walk fields (a.k.a. local receptive fields) with random walk paths to depict subgraph structures and advocate graph scalability. To express the internal variations of a walk field, Gaussian mixture models are introduced to encode principal components of walk paths therein. As an analogy to a standard convolution kernel on image, Gaussian models implicitly coordinate those unordered vertices/nodes and edges in a local receptive field after projecting to the gradient space of Gaussian parameters. We further stack graph coarsening upon Gaussian encoding by using dynamic clustering, such that high-level semantics of graph can be well learned like the conventional pooling on image. The experimental results on several public datasets demonstrate the superiority of our proposed WSC method over many state-of-the-arts for graph classification. Copyright © 2018, The Authors. All rights reserved.

关键词： Random processes

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UAV assisted bridge defect inspection system 3rd

UAV assisted bridge defect inspection system

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3rd International Conference on Intelligence Science, ICIS 2018

作者： Yang, Shuzhan Shen, Zhen Wang, Xiao Bai, Tianxiang Ji, Yingliang Jiang, Yuyi Liu, Xiwei Dong, Xisong Li, Chuanfu Han, Qi Lu, Jian Xiong, Gang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266109 China College of Engineering Peking University Beijing100871 China University of Chinese Academy of Sciences Beijing100049 China Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Expressway Qingdao266041 China Guangdong Launca Medical Device Technology Co. Ltd. Dongguan523808 China

ISBN: (纸本)9783030013127

Traditional bridge inspection methods require lane closures, inspection equipment, and most importantly the experiences and knowledge of the inspectors. This increases not only the inspection cost and time, but also the risk to the travelling public. Due to the lengthy and costly traditional bridge inspection methods, there has been an increasing backlog of inspection activities. In this research, we design an unmanned aerial vehicle (UAV) assisted bridge defect inspection system, in which a UAV can capture the image and transmit the information to the ground station for further analysis. The system can be divided into 2 subsystems: electromechanics & communication system, and image processing system. The electromechanics & communication systems ensure the self-locating, flight control, image transmission, and human intervention functions. The image processing system performs the image preprocessing, defect extraction, and provides the inspection report. This system, if put into practice, can save the cost up to 70%. We believe that the UAV assisted bridge inspection can be popular in the future. © IFIP International Federation for Information Processing 2018.

关键词： image processing

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