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SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals

Brain informatics 2021年第1期8卷 1-19页

作者： Fabietti, Marcos Mahmud, Mufti Lotfi, Ahmad Kaiser, M. Shamim Averna, Alberto Guggenmos, David J. Nudo, randolph J. Chiappalone, Michela Chen, Jianhui Department of Computer Science Nottingham Trent University Clifton Lane NottinghamNG11 8NS United Kingdom Medical Technologies Innovation Facility Nottingham Trent University Clifton Lane NottinghamNG11 8NS United Kingdom Computing and Informatics Research Centre Nottingham Trent University Clifton Lane NottinghamNG11 8NS United Kingdom Institute of Information Technology Jahangirnagar University Savar Dhaka1342 Bangladesh Department of Health Sciences University of Milan Via di Rudinì 8 Milan20142 Italy Department of Physical Medicine and Rehabilitation University of Kansas Medical Center 3901 Rainbow Blvd Kansas City66160 United States Department of informatics Bioengineering Robotics and System Engineering-DIBRIS University of Genova Via All’Opera Pia 13 Genoa16145 Italy Faculty of Information Technology International WIC Institute Beijing University of Technology Beijing100124 China Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing100124 China

Neuronal signals generally represent activation of the neuronal networks and give insights into brain functionalities. They are considered as fingerprints of actions and their processing across different structures of the brain. These recordings generate a large volume of data that are susceptible to noise and artifacts. Therefore, the review of these data to ensure high quality by automatically detecting and removing the artifacts is imperative. Toward this aim, this work proposes a custom-developed automatic artifact removal toolbox named, SANTIA (SigMate Advanced: a Novel Tool for Identification of Artifacts in Neuronal Signals). Developed in Matlab, SANTIA is an open-source toolbox that applies neural network-based machine learning techniques to label and train models to detect artifacts from the invasive neuronal signals known as local field potentials. © 2021, The Author(s).

关键词： Neural networks

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Various Deep Learning Methods for Hyperspectral Images

Various Deep Learning Methods for Hyperspectral Images

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2020 International Conference on Computing and Information technology, ICCIT 2020

作者： Sivaram, M. Shobana, S. Jeya Khan, Mujeebudheen ramakrishnan, Jayabrabu Goel, Pallavi Murghai Maseleno, Andino Lebanese French University Department of Computer Networking Erbil Kurdistan Region Iraq College of Science Knowledge University Department of Computer Science Erbil Kurdistan Region Iraq Ibra College of Technology Department of Electrical and Electronics Oman Jazan University College of Computer Science and Information Technology Department of Information Technology and Security Saudi Arabia Galgotias University School of Computing Science and Engineering Greater Noida India Universiti Tenaga Nasional Institute of Informatics and Computing Energy Department of Business Administration and Information System Malaysia

ISBN: (纸本)9781728126807

Hyperspectral imagery is widely used in remote sensing applications that take into account thousands of spectral channel compositions over a single scene. Hyperspectral imagery requires accurate models of learning to extract the hyperspectral features in an image. Due to the presence of its spatial and spectral resolution, the image learning model presents a core challenge due to its complicated nature of image frames. In order to assist it during the learning process, several attempts have been made to address its complicated nature. However, these methods failed to provide the hyperspectral imagery with a deeper understanding. Because of the presence of mixed pixels, limited training samples and redundant data, the utilization of deep learning techniques addresses the problems. The deep learning process addresses the complex image data relationship. In this paper, various deep learning methods are studied which are used for the learning of hyperspectral imagery. Initially, we present an overview on various deep learning methods for various image processing techniques. A system review is then carried out on various hyperspectral image learning models based on deep learning. © 2020 IEEE.

关键词： Remote sensing

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A comparison of approaches to improve worst-case predictive model performance over patient subpopulations

arXiv

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arXiv 2021年

作者： Pfohl, Stephen R. Zhang, Haoran Xu, Yizhe Foryciarz, Agata Ghassemi, Marzyeh Shah, Nigam H. Stanford Center for Biomedical Informatics Research Stanford University StanfordCA94305 United States Department of Computer Science University of Toronto TorontoON Canada Department of Computer Science Stanford University StanfordCA94305 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Institute for Medical and Evaluative Sciences Massachusetts Institute of Technology CambridgeMA02139 United States

Predictive models for clinical outcomes that are accurate on average in a patient population may underperform drastically for some subpopulations, potentially introducing or reinforcing inequities in care access and quality. Model training approaches that aim to maximize worst-case model performance across subpopulations, such as distributionally robust optimization (DRO), attempt to address this problem without introducing additional harms. We conduct a large-scale empirical study of DRO and several variations of standard learning procedures to identify approaches for model development and selection that consistently improve disaggregated and worst-case performance over subpopulations compared to standard approaches for learning predictive models from electronic health records data. In the course of our evaluation, we introduce an extension to DRO approaches that allows for specification of the metric used to assess worst-case performance. We conduct the analysis for models that predict in-hospital mortality, prolonged length of stay, and 30-day readmission for inpatient admissions, and predict in-hospital mortality using intensive care data. We find that, with relatively few exceptions, no approach performs better, for each patient subpopulation examined, than standard learning procedures using the entire training dataset. These results imply that when it is of interest to improve model performance for patient subpopulations beyond what can be achieved with standard practices, it may be necessary to do so via techniques that implicitly or explicitly increase the effective sample size. Copyright © 2021, The Authors. All rights reserved.

关键词： Optimization

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Node based Label Propagation for Bitcoin Transaction Pattern Identification Over Similar Community

Node based Label Propagation for Bitcoin Transaction Pattern...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Manika Manwal Ahmad Murtaza Alvi Nagendra Kumar Turaga Amit Mittal Richard Rivera Sheshang Degadwala CSE Graphic Era Hill University Dehradun India CSE Graphic Era deemed to be University Dehradun India Department of Business Administration College of Finance Saudi Electronic University Riyadh Saudi Arabia Department of Management Studies Vignan's Foundation for Science Technology and Research (Deemed to be University) Department of Allied Science Graphic Era Hill University Bhimtal Department of Informatics and Computer Science Escuela Politecnica Nacional. Quito Ecuador Sigma Institute of Engineering Engineering Block Sigma Group of Institutes Bakrol Vadodara Gujarat

People have conducted decentralized transfer transactions through Bitcoin addresses ever since the Bitcoin system launched online, drastically improving the convenience of transactions. Simultaneously, peer-to-peer transaction logs have become a subject of study. However, it requires a significant amount of time and computing capacity to examine the entire network directly, and it is not conducive to observing the transaction mode within the entity. Consequently, it is possible to construct and analyses the transaction network based on the entity service community and further investigate entity behavior and Bitcoin entities within the service community. A central node-based label propagation algorithm is proposed by enhancing the conventional label propagation algorithm, which divides the Bitcoin entity transaction network into districts and analyses the core communities, including exchanges and mining pools. The transaction method is realistic and straightforward to comprehend. The enhanced label propagation algorithm can converge more quickly and reduce the randomness of the results of community division. The experimental results demonstrate that the internal transaction modalities of various services vary. The simplified display makes the Bitcoin transaction network more readable.

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Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation

arXiv

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arXiv 2022年

作者： Nan, Yang Del Ser, Javier Tang, Zeyu Tang, Peng Xing, Xiaodan Fang, Yingying Herrera, Francisco Pedrycz, Witold Walsh, Simon Yang, Guang National Heart and Lung Institute Imperial College London London United Kingdom Department of Communications Engineering University of the Basque Country UPV/EHU Bilbao Spain Derio Spain Department of Informatics Technical University of Munich Germany University of Granada Granada Spain Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Department of Electrical and Computer Engineering Faculty of Engineering King Abgudulaziz University Jeddah Saudi Arabia The Systems Research Institute Polish Academy of Sciences Warsaw Poland Royal Brompton Hospital Sydney Street London United Kingdom

Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases, while its manual delineation is unduly burdensome. To alleviate this time-consuming and potentially subjective manual procedure, researchers have proposed methods to automatically segment airways from computerized tomography (CT) images. However, some small-sized airway branches (e.g., bronchus and terminal bronchioles) significantly aggravate the difficulty of automatic segmentation by machine learning models. In particular, the variance of voxel values and the severe data imbalance in airway branches make the computational module prone to discontinuous and false-negative predictions. especially for cohorts with different lung diseases. Attention mechanism has shown the capacity to segment complex structures, while fuzzy logic can reduce the uncertainty in feature representations. Therefore, the integration of deep attention networks and fuzzy theory, given by the fuzzy attention layer, should be an escalated solution for better generalization and robustness. This paper presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network and a comprehensive loss function to enhance the spatial continuity of airway segmentation. The deep fuzzy set is formulated by a set of voxels in the feature map and a learnable Gaussian membership function. Different from the existing attention mechanism, the proposed channel-specific fuzzy attention addresses the issue of heterogeneous features in different channels. Furthermore, a novel evaluation metric is proposed to assess both the continuity and completeness of airway structures. The efficiency, generalization and robustness of the proposed method have been proved by training on normal lung disease while testing on datasets of lung cancer, COVID-19 and pulmonary fibrosis. © 2022, CC BY-NC-ND.

关键词： COVID-19

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Unsupervised Domain Adaptation on Person Re-Identification via Dual-level Asymmetric Mutual Learning

arXiv

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arXiv 2023年

作者： Wu, Qiong Li, Jiahan Dai, Pingyang Ye, Qixiang Cao, Liujuan Wu, Yongjian Ji, Rongrong Institute of Artificial Intelligence The Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen University Xiamen361005 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221000 China The Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen University Xiamen361005 China The Peng Cheng Laboratory Shenzhen518066 China The School of Electronics Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China The Media Analytics and Computing Lab Department of Computer Science School of Informatics Xiamen University Xiamen361005 China The Youtu Laboratory Tencent Shanghai200233 China The Fujian Engineering Research Center of Trusted Artificial Intelligence Analysis and Application Institute of Artificial Intelligence Xiamen University Xiamen361005 China

Unsupervised domain adaptation person re-identification (Re-ID) aims to identify pedestrian images within an unlabeled target domain with an auxiliary labeled source-domain dataset. Many existing works attempt to recover reliable identity information by considering multiple homogeneous networks. And take these generated labels to train the model in the target domain. However, these homogeneous networks identify people in approximate subspaces and equally exchange their knowledge with others or their mean net to improve their ability, inevitably limiting the scope of available knowledge and putting them into the same mistake. This paper proposes a Dual-level Asymmetric Mutual Learning method (DAML) to learn discriminative representations from a broader knowledge scope with diverse embedding spaces. Specifically, two heterogeneous networks mutually learn knowledge from asymmetric subspaces through the pseudo label generation in a hard distillation manner. The knowledge transfer between two networks is based on an asymmetric mutual learning manner. The teacher network learns to identify both the target and source domain while adapting to the target domain distribution based on the knowledge of the student. Meanwhile, the student network is trained on the target dataset and employs the ground-truth label through the knowledge of the teacher. Extensive experiments in Market-1501, CUHK-SYSU, and MSMT17 public datasets verified the superiority of DAML over state-of-the-arts. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Model-based testing of Apache ZooKeeper: Fundamental API usage and watchers

Model-based testing of Apache ZooKeeper: Fundamental API usa...

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作者： Artho, Cyrille Banzai, Kazuaki Gros, Quentin Rousset, Guillaume Ma, Lei Kitamura, Takashi Hagiya, Masami Tanabe, Yoshinori Yamamoto, Mitsuharu School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Institute of Information Technology Research National Institute of Advanced Industrial Science and Technology Osaka Japan Department of Computer Science The University of Tokyo Tokyo Japan Polytech Nantes University of Nantes Nantes France Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan Department of Library Archival and Information Studies Tsurumi University Yokohama Japan Department of Mathematics and Informatics Chiba University Chiba Japan

In this paper, we extend work on model-based testing for Apache ZooKeeper, to handle watchers (triggers) and improve scalability. In a distributed asynchronous shared storage like ZooKeeper, watchers deliver notifications on state changes. They are difficult to test because watcher notifications involve an initial action that sets the watcher, followed by another action that changes the previously seen state. We show how to generate test cases for concurrent client sessions executing against ZooKeeper with the tool Modbat. The tests are verified against an oracle that takes into account all possible timings of network communication. The oracle has to verify that there exists a chain of events that triggers both the initial callback and the subsequent watcher notification. We show in detail how the oracle computes whether watch triggers are correct and how the model was adapted and improved to handle these features. Together with a new search improvement that increases both speed and accuracy, we are able to verify large test setups and confirm several defects with our model. Copyright © 2019 John Wiley & Sons, Ltd.

关键词： Model checking

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Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-Guided radiotherapy 1st

Comparative Analysis of nnUNet and MedNeXt for Head and Neck...

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1st Challenge on Head and Neck Tumor Segmentation for MRI-Guided Applications, HNTS-MRG 2024, Held in Conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Moradi, Nikoo Ferreira, Andre Puladi, Behrus Kleesiek, Jens Fatemizadeh, Emad Luijten, Gijs Alves, Victor Egger, Jan Department of Electrical Engineering Sharif University of Technology Tehran Iran University Medicine Essen Girardetstraße 2 Essen45131 Germany Center Algoritmi/LASI University of Minho Braga4710-057 Portugal Computer Algorithms for Medicine Laboratory Graz Austria Department of Oral and Maxillofacial Surgery University Hospital RWTH Aachen Aachen Germany Institute of Medical Informatics University Hospital RWTH Aachen Aachen Germany Essen Germany Partner Site Essen Essen Germany Department of Physics TU Dortmund University Dortmund Germany University Medicine Essen Essen Germany Graz University of Technology Inffeldgasse 16/II Graz8010 Austria

ISBN: (纸本)9783031832734

radiation therapy (RT) is essential in treating head and neck cancer (HNC), with magnetic resonance imaging(MRI)-guided RT offering superior soft tissue contrast and functional imaging. However, manual tumor segmentation is time-consuming and complex, and there- fore remains a challenge. In this study, we present our solution as team TUMOR to the HNTS-MRG24 MICCAI Challenge which is focused on automated segmentation of primary gross tumor volumes (GTVp) and metastatic lymph node gross tumor volume (GTVn) in pre-RT and mid- RT MRI images. We utilized the HNTS-MRG2024 dataset, which con- sists of 150 MRI scans from patients diagnosed with HNC, including original and registered pre-RT and mid-RT T2-weighted images with corresponding segmentation masks for GTVp and GTVn. We employed two state-of-the-art models in deep learning, nnUNet and MedNeXt. For Task 1, we pretrained models on pre-RT registered and mid-RT images, followed by fine-tuning on original pre-RT images. For Task 2, we com- bined registered pre-RT images, registered pre-RT segmentation masks, and mid-RT data as a multi-channel input for training. Our solution for Task 1 achieved 1st place in the final test phase with an aggregated Dice Similarity Coefficient of 0.8254, and our solution for Task 2 ranked 8th with a score of 0.7005. The proposed solution is publicly available at Github Repository. © The Author(s) 2025.

关键词： Image segmentation

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Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023

Predicting Neurological Recovery from Coma After Cardiac Arr...

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computers in Cardiology (CinC)

作者： Matthew A Reyna Edilberto Amorim Reza Sameni James Weigle Andoni Elola Ali Bahrami rad Salman Seyedi Hyeokhyen Kwon Wei-Long Zheng Mohammad M Ghassemi Michel JAM van Putten Jeannette Hofmeijer Nicolas Gaspard Adithya Sivaraju Susan T Herman Jong Woo Lee M Brandon Westover Gari D Clifford Department of Biomedical Informatics Emory University USA Department of Neurology Weill Institute for Neurosciences University of California San Francisco USA Department of Neurology Massachusetts General Hospital USA Department of Electronic Technology University of the Basque Country UPV/EHU Spain Department of Computer Science and Engineering Shanghai Jiao Tong University China Department of Computer Science and Engineering Michigan State University USA Clinical Neurophysiology Group University of Twente The Netherlands Department of Neurology and Clinical Neurophysiology Medisch Spectrum Twente The Netherlands Department of Neurology Rijnstate Hospital The Netherlands Department of Neurology Yale School of Medicine USA Service de Neurologie Hôpital Universitaire de Bruxelles and Université Libre de Bruxelles Belgium Department of Neurology Barrow Neurological Institute Comprehensive Epilepsy Center USA Department of Neurology Brigham and Women's Hospital USA Department of Neurology Beth Israel Deaconess Medical Center USA McCance Center for Brain Health Massachusetts General Hospital USA Harvard Medical School USA Department of Biomedical Engineering Georgia Institute of Technology USA

The George B. Moody PhysioNet Challenge 2023 invited teams to develop algorithmic approaches for predicting the recovery of comatose patients after cardiac arrest. A patient's prognosis after the return of spontaneous circulation informs treatment, including the continuation or withdrawal of life support. Brain monitoring with an electroencephalogram (EEG) can improve the objectivity of a prognosis, but EEG interpretation requires clinical expertise. The algorithmic analysis of EEGs can potentially improve the accuracy and accessibility of prognoses, but existing work is limited by small and homogeneous datasets. The PhysioNet Challenge 2023 contributed to addressing these problems. It introduced the International Cardiac Arrest REsearch consortium (I-CARE) dataset, which is a large, multi-center collection of EEGs, other physiological data, and clinical outcomes, with over 57,000 hours of data from 1,020 patients from seven hospitals. It required teams to submit their complete training and inference code to improve the reproducibility and generalizability of their research. A total of 111 teams participated in the Challenge, contributing diverse approaches from academic, clinical, and industry participants worldwide.

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Preface

Preface

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作者： Dehuri, Satchidananda Mishra, Bhabani Shankar Prasad Mallick, Pradeep Kumar Cho, Sung-Bae Favorskaya, Margarita N. Department of Information and Communication Technology Fakir Mohan University Odisha Balasore India School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar India Institute of Informatics and Telecommunications Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia Department of Computer Science Yonsei University Seoul Korea Republic of

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