检索结果-内蒙古大学图书馆

TechRxiv 2023年

作者： Franco-Barranco, Daniel Lin, Zudi Jang, Won-Dong Wang, Xueying Shen, Qijia Yin, Wenjie Fan, Yutian Li, Mingxing Chen, Chang Xiong, Zhiwei Xin, Rui Liu, Hao Chen, Huai Li, Zhili Zhao, Jie Chen, Xuejin Pape, Constantin Conrad, Ryan De Folter, Jozefus Nightingale, Luke Jones, Martin L. Liu, Yanling Ziaei, Dorsa Huschauer, Stephan Arganda-Carreras, Ignacio Pfister, Hanspeter Wei, Donglai The Department of Computer Science and Artificial Intelligence University of the Basque Country Donostia-San Sebastian Spain San Sebastian Spain Ikerbasque Basque Foundation for Science Bilbao Spain Biofisika Institute CSIC UPV/EHU Bilbao Spain Harvard University All-ston MA United States The Department of Molecular and Cellular Biology Harvard University CambridgeMA United States The Wellcome Centre for Integrative Neuroimaging FMRIB Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom University of Science and Technology of China Anhui China The Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai China The National Engineering Laboratory for Brain-inspired Intelligence Technology and Application University of Science and Technology of China Anhui China The Georg-August University Goettingen Germany The Center for Molecular Microscopy Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda United States The Cancer Research Technology Program Frederick National Laboratory for Cancer Research Frederick United States The Francis Crick Institute London United Kingdom The Advanced Biomedical Computational Science Group Frederick National Laboratory for Cancer Research FrederickMD United States The Computer Science Department Boston College Chestnut Hill MA United States

In this paper, we present the results of the MitoEM challenge on mitochondria 3D instance segmentation from electron microscopy images, organized in conjunction with the IEEE-ISBI 2021 conference. Our benchmark dataset consists of two large-scale 3D volumes, one from human and one from rat cortex tissue, which are 3,600 times larger than previously used datasets. At the time of paper submission, 257 participants had registered for the challenge, 14 teams had submitted their results, and six teams participated in the challenge workshop. Here, we present eight top-performing approaches from the challenge participants, along with our own baseline strategies. Posterior to the challenge, annotation errors in the ground truth were corrected without altering the final ranking. Additionally, we present a retrospective evaluation of the scoring system which revealed that (1) the challenge metric was permissive with the false positive predictions and (2) the size-based grouping of instances did not correctly categorize mitochondria of interest. Thus, we propose a new scoring system that better reflects the correctness of the segmentation results. Although several of the top methods are compared favorably to our own baselines, substantial errors remain unsolved for mitochondria with challenging morphologies. Thus, the challenge remains open for submission and automatic evaluation, with all volumes available for download. © 2023, CC BY.

关键词： Electron microscopy

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COMIC: Towards a compact image captioning model with attention

arXiv

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arXiv 2019年

作者： Tan, Jia Huei Chan, Chee Seng Chuah, Joon Huang Center of Image and Signal Processing Department of Artificial Intelligence University of Malaya Kuala Lumpur50603 Malaysia Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia

Recent works in image captioning have shown very promising raw performance. However, we realize that most of these encoder-decoder style networks with attention do not scale naturally to large vocabulary size, making them difficult to be deployed on embedded system with limited hardware resources. This is because the size of word and output embedding matrices grow proportionally with the size of vocabulary, adversely affecting the compactness of these networks. To address this limitation, this paper introduces a brand new idea in the domain of image captioning. That is, we tackle the problem of compactness of image captioning models which is hitherto unexplored. We showed that, our proposed model, named COMIC for COMpact image Captioning, achieves comparable results in five common evaluation metrics with state-of-The-Art approaches on both MS-COCO and InstaPIC-1.1M datasets despite having an embedding vocabulary size that is 39×-99× smaller. The source code and models are available at: https://***/jiahuei/COMIC-Compact-image-Captioning-with-Attention. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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Overcoming thresholds – Utilizing convolutional neural networks for predicting individual thrombectomy response in acute ischemic stroke

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Clinical Neurophysiology 2024年 159卷 e8-e8页

作者： von Braun, M. Scholl, K. Peter, L. Welle, F. Schneider, H.R. Wawrzyniak, M. Kaiser, D.P.O. Henkelmann, J. Prasse, G. Kellner, E. Reisert, M. Klingbeil, J. Stockert, A. Lobsien, D. Hoffmann, K. Scheuermann, G. Gilmann, C. Saur, D. Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI Dresden/Leipzig) Leipzig University Germany Neuroimaging Laboratory Department of Neurology University of Leipzig Medical Center Germany Institute of Neuroradiology University Medical Center Carl Gustav Carus Dresden Germany Department of Radiology University of Leipzig Medical Center Germany Institute of Neuroradiology University of Leipzig Medical Center Germany Medical Physics Department of Diagnostic and Interventional Radiology Medical Center – University of Freiburg Germany Institute for Diagnostic and Interventional Radiology and Neuroradiology Helios Hospital Erfurt Germany Image and Signal Processing Group Institute for Informatics Leipzig University Germany

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Affine Non-negative Collaborative Representation Based Pattern Classification

arXiv

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arXiv 2020年

作者： Yin, He-Feng Wu, Xiao-Jun Feng, Zhen-Hua Kittler, Josef School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

—During the past decade, representation-based classification methods have received considerable attention in pattern recognition. In particular, the recently proposed non-negative representation based classification (NRC) method has been reported to achieve promising results in a wide range of classification tasks. However, NRC has two major drawbacks. First, there is no regularization term in the formulation of NRC, which may result in unstable solution and misclassification. Second, NRC ignores the fact that data usually lies in a union of multiple affine subspaces, rather than linear subspaces in practical applications. To address the above issues, this paper presents an affine non-negative collaborative representation (ANCR) model for pattern classification. To be more specific, ANCR imposes a regularization term on the coding vector. Moreover, ANCR introduces an affine constraint to better represent the data from affine subspaces. The experimental results on several benchmarking datasets demonstrate the merits of the proposed ANCR method. The source code of our ANCR is publicly available at https://***/yinhefeng/ANCR. Copyright © 2020, The Authors. All rights reserved.

关键词： Vectors

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Priming with specific context improves large language model performance on nuclear cardiology board preparation test

引用

Journal of Nuclear Cardiology 2025年

作者： Waseem Hijazi Valerie Builoff Aditya Killekar Aakash Shanbhag Robert JH. Miller Damini Dey Joanna X. Liang Kathleen Flood Daniel Berman Jamieson M. Bourque Lawrence M. Phillips Panithaya Chareonthaitawee Piotr J. Slomka Departments of Medicine (Division of Artificial Intelligence in Medicine) Imaging and Biomedical Sciences Cedars-Sinai Medical Center Los Angeles CA USA Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA USA Department of Cardiac Sciences University of Calgary Calgary AB Canada American Society of Nuclear Cardiology Fairfax Virginia USA Division of Cardiovascular Medicine and Radiology University of Virginia Health System Charlottesville VA USA Leon H. Charney Division of Cardiology Department of Medicine NYU Grossman School of Medicine New York NY USA Department of Cardiovascular Medicine Mayo Clinic Rochester MN USA

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image synthesis with adversarial networks: A comprehensive survey and case studies

arXiv

引用

arXiv 2020年

作者： Shamsolmoali, Pourya Zareapoor, Masoumeh Granger, Eric Zhou, Huiyu Wang, Ruili Emre Celebi, M. Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Laboratoire d’imagerie De vision et d’intelligence artificielle École de technologie supérieure Montreal Canada School of Informatics University of Leicester United Kingdom School of Natural and Computational Sciences Massey University Auckland New Zealand Department of Computer Science University of Central Arkansas United States

Generative Adversarial Networks (GANs) have been extremely successful in various application domains such as computer vision, medicine, and natural language processing. Moreover, transforming an object or person to a desired shape become a well-studied research in the GANs. GANs are powerful models for learning complex distributions to synthesize semantically meaningful samples. However, there is a lack of comprehensive review in this field, especially lack of a collection of GANs loss-variant, evaluation metrics, remedies for diverse image generation, and stable training. Given the current fast GANs development, in this survey, we provide a comprehensive review of adversarial models for image synthesis. We summarize the synthetic image generation methods, and discuss the categories including image-to-image translation, fusion image generation, label-to-image mapping, and text-to-image translation. We organize the literature based on their base models, developed ideas related to architectures, constraints, loss functions, evaluation metrics, and training datasets. We present milestones of adversarial models, review an extensive selection of previous works in various categories, and present insights on the development route from the model-based to data-driven methods. Further, we highlight a range of potential future research directions. One of the unique features of this review is that all software implementations of these GAN methods and datasets have been collected and made available in one place at https://***/pshams55/GAN-CaseStudy. © 2020, CC BY-NC-ND.

关键词： Generative adversarial networks

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Advancing Cross-Subject Domain Generalization in Brain-Computer Interfaces with Multi-Adversarial Strategies

引用

IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Liu, Yici Qin, Lang Chen, Xin Jeannes, Regine Le Bouquin Coatrieux, Jean Louis Shu, Huazhong Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing210096 China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Nanjing210096 China RennesF-3502 France Southeast University INSERM Nanjing210096 China The First Affiliated Hospital With Nanjing Medical University Department of Radiology China Universit de Rennes 1 Laboratoire Traitement du Signal et de l’Image Rennes35000 France Centre de Recherche en Information Biomedicale Sino-Francais Rennes35042 France National Institute for Health and Medical Research Rennes35000 France

A cross-subject domain generalization (DG) approach with multi-adversarial strategies (DGMA) is introduced to reduce brain-computer interfaces (BCIs) systems’ dependency on high-quality, subject-specific EEG data, making it adaptable to unseen domains. DGMA leverages annotated training data from other subjects and consists of three modules: (1) Prefeature extraction, enhancing EEG signal separability through preprocessing, data augmentation, and tangent space mapping;(2) Distribution feature updater, aligning inter-subject feature distributions with marginal maximum mean discrepancy (MMD);(3) Multi-adversarial training, initially using gradient reversal layer (GRL) to amplify domain differences and classification loss, allowing the model to learn diverse domain-specific features before minimizing these differences to balance domain transferability and discriminability. DGMA is capable of better capturing domain-specific features while achieving stronger generalization compared to traditional methods focused solely on minimizing domain differences. Validated on four motor imagery datasets, DGMA achieved state-of-the-art accuracies of 76.1% on BCI Competition IV 2a and 72.4% on the 002-2014 dataset. Additional tests on a private fatigue dataset and the SEED dataset yielded accuracies of 99.5% and 86.6%, respectively. © 1963-2012 IEEE.

关键词： signal reconstruction

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Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan images

arXiv

引用

arXiv 2020年

作者： Rajinikanth, V. Dey, Nilanjan Raj, Alex Noel Joseph Hassanien, Aboul Ella Santosh, K.C. Raja, N. Sri Madhava Department of Electronics and Instrumentation Engineering St. Joseph’s College of Engineering Chennai600119 India Department of Information Technology Techno India College of Technology KolkataWest Bengal700156 India Key Laboratory of Digital Signal and Image Processing of Guangdong Province Department of Electronic Engineering College of Engineering Shantou University Shantou Guangdong China Faculty of Computers and Artificial Intelligence Cairo University Egypt Department of Computer Science University of South Dakota 414 E Clark St VermillionSD57069 United States

Pneumonia is one of the foremost lung diseases and untreated pneumonia will lead to serious threats for all age groups. The proposed work aims to extract and evaluate the Coronavirus disease (COVID-19) caused pneumonia infection in lung using CT scans. We propose an image-assisted system to extract COVID-19 infected sections from lung CT scans (coronal view). It includes following steps: (i) Threshold filter to extract the lung region by eliminating possible artifacts;(ii) image enhancement using Harmony-Search-Optimization and Otsu thresholding;(iii) image segmentation to extract infected region(s);and (iv) Region-of-interest (ROI) extraction (features) from binary image to compute level of severity. The features that are extracted from ROI are then employed to identify the pixel ratio between the lung and infection sections to identify infection level of severity. The primary objective of the tool is to assist the pulmonologist not only to detect but also to help plan treatment process. As a consequence, for mass screening processing, it will help prevent diagnostic burden. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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Understanding metric-related pitfalls in image analysis validation

arXiv

引用

arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： Computer vision

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AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans for mortality prediction: a multicentre study

引用

The Lancet. Digital health 2025年第5期7卷 100862页

作者： Jirong Yi Anna M Marcinkiewicz Aakash Shanbhag Robert J H Miller Jolien Geers Wenhao Zhang Aditya Killekar Nipun Manral Mark Lemley Mikolaj Buchwald Jacek Kwiecinski Jianhang Zhou Paul B Kavanagh Joanna X Liang Valerie Builoff Terrence D Ruddy Andrew J Einstein Attila Feher Edward J Miller Albert J Sinusas Daniel S Berman Damini Dey Piotr J Slomka Departments of Medicine (Division of Artificial Intelligence in Medicine) Biomedical Sciences and Imaging Cedars-Sinai Medical Center Los Angeles CA USA. Departments of Medicine (Division of Artificial Intelligence in Medicine) Biomedical Sciences and Imaging Cedars-Sinai Medical Center Los Angeles CA USA Center of Radiological Diagnostics National Medical Institute of the Ministry of the Interior and Administration Warsaw Poland. Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA USA. Department of Cardiac Sciences University of Calgary Calgary AB Canada. Department of Cardiology Centrum voor Hart-en Vaatziekten (CHVZ) Universitair Ziekenhuis Brussel (UZ Brussel) Vrije Universiteit Brussel (VUB) Brussels Belgium. Department of Interventional Cardiology and Angiology Institute of Cardiology Warsaw Poland. Division of Cardiology University of Ottawa Heart Institute Ottawa ON Canada. Division of Cardiology Department of Medicine and Department of Radiology Columbia University Irving Medical Center and New York-Presbyterian Hospital New York NY USA. Section of Cardiovascular Medicine Department of Internal Medicine Yale University School of Medicine New Haven CT USA. Departments of Medicine (Division of Artificial Intelligence in Medicine) Biomedical Sciences and Imaging Cedars-Sinai Medical Center Los Angeles CA USA. Electronic address: piotr.slomka@***.

BACKGROUND:CT attenuation correction (CTAC) scans are routinely obtained during cardiac perfusion imaging, but currently only used for attenuation correction and visual calcium estimation. We aimed to develop a novel artificial intelligence (AI)-based approach to obtain volumetric measurements of chest body composition from CTAC scans and to evaluate these measures for all-cause mortality risk stratification. METHODS:We applied AI-based segmentation and image-processing techniques on CTAC scans from a large international image-based registry at four sites (Yale University, University of Calgary, Columbia University, and University of Ottawa), to define the chest rib cage and multiple tissues. Volumetric measures of bone, skeletal muscle, subcutaneous adipose tissue, intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT), and epicardial adipose tissue (EAT) were quantified between automatically identified T5 and T11 vertebrae. The independent prognostic value of volumetric attenuation and indexed volumes were evaluated for predicting all-cause mortality, adjusting for established risk factors and 18 other body composition measures via Cox regression models and Kaplan-Meier curves. FINDINGS:The end-to-end processing time was less than 2 min per scan with no user interaction. Between 2009 and 2021, we included 11 305 participants from four sites participating in the REFINE SPECT registry, who underwent single-photon emission computed tomography cardiac scans. After excluding patients who had incomplete T5-T11 scan coverage, missing clinical data, or who had been used for EAT model training, the final study group comprised 9918 patients. 5451 (55%) of 9918 participants were male and 4467 (45%) of 9918 participants were female. Median follow-up time was 2·48 years (IQR 1·46-3·65), during which 610 (6%) patients died. High VAT, EAT, and IMAT attenuation were associated with an increased all-cause mortality risk (adjusted hazard ratio 2·39, 95% CI 1·92-2·96; p<

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