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Wyszukujesz frazę ""deep learning"" wg kryterium: Temat


Tytuł :
From community-acquired pneumonia to COVID-19: a deep learning-based method for quantitative analysis of COVID-19 on thick-section CT scans.
Autorzy :
Li Z; College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China.; Hunan Key Laboratory for Image Measurement and Vision Navigation, Changsha, Hunan, China.
Zhong Z; Department of Radiology, The First Hospital of Changsha City, Changsha, China.
Li Y; College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China.; Hunan Key Laboratory for Image Measurement and Vision Navigation, Changsha, Hunan, China.
Zhang T; GROW School for Oncology and Development Biology, Maastricht University, P. O. Box 616, 6200, MD, Maastricht, The Netherlands.; Department of Radiology, Netherlands Cancer Institute (NKI), Plesmanlaan 121, 1066, CX, Amsterdam, The Netherlands.
Gao L; PingAn Technology, Shenzhen, China.
Jin D; PAII Inc., Bethesda, MD, USA.
Sun Y; Department of Electrical Engineering, Eindhoven University of Technology, 5600, MB, Eindhoven, The Netherlands.
Ye X; Department of Radiotherapy, The First Affiliated Hospital, Zhejiang University, Zhejiang, Hangzhou, China.
Yu L; Hunan LanXi Biotechnology Ltd., Changsha, China.
Hu Z; Hunan Cancer Hospital, the Affiliated Cancer Hospital of Xiangya Medical School, Central South University, Changsha, China.
Xiao J; PingAn Technology, Shenzhen, China.
Huang L; PingAn Technology, Shenzhen, China. .
Tang Y; Department of Respiratory Medicine, The First Hospital of Changsha City, Changsha, China. .
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Źródło :
European radiology [Eur Radiol] 2020 Dec; Vol. 30 (12), pp. 6828-6837. Date of Electronic Publication: 2020 Jul 18.
Typ publikacji :
Journal Article
MeSH Terms :
Betacoronavirus*
Deep Learning*
Community-Acquired Infections/*diagnosis
Coronavirus Infections/*diagnosis
Lung/*diagnostic imaging
Pneumonia/*diagnosis
Pneumonia, Viral/*diagnosis
Tomography, X-Ray Computed/*methods
Artificial Intelligence ; China/epidemiology ; Disease Progression ; Female ; Humans ; Male ; Middle Aged ; Pandemics ; ROC Curve ; Retrospective Studies
SCR Disease Name :
COVID-19
Czasopismo naukowe
Tytuł :
A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images.
Autorzy :
Ni Q; Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
Sun ZY; Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
Qi L; Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
Chen W; Department of Medical Imaging, Taihe Hospital, Shiyan, 442008, Hubei, China.
Yang Y; Department of Medical Imaging, Wuhan First Hospital, Wuhan, 430022, Hubei, China.
Wang L; Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
Zhang X; Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
Yang L; Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
Fang Y; Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
Xing Z; Deepwise AI Lab, Beijing, 100080, China.
Zhou Z; School of Electronics Engineering and Computer Science, Peking University, Beijing, 10080, China.
Yu Y; Department of Computer Science, The University of Hong Kong, Pok Fu Lam, Hong Kong.
Lu GM; Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
Zhang LJ; Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China. .; Department of Medical Imaging, Medical Imaging Center, Nanjing Clinical School, Southern Medical University, 305 Zhongshan East Road, Xuanwu District, Nanjing, 210002, Jiangsu, China. .
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Źródło :
European radiology [Eur Radiol] 2020 Dec; Vol. 30 (12), pp. 6517-6527. Date of Electronic Publication: 2020 Jul 02.
Typ publikacji :
Comparative Study; Journal Article; Multicenter Study
MeSH Terms :
Algorithms*
Betacoronavirus*
Deep Learning*
Pandemics*
Coronavirus Infections/*diagnosis
Coronavirus Infections/*epidemiology
Lung/*diagnostic imaging
Pneumonia, Viral/*diagnosis
Pneumonia, Viral/*epidemiology
Tomography, X-Ray Computed/*methods
China/epidemiology ; Female ; Humans ; Male ; Middle Aged
SCR Disease Name :
COVID-19
Czasopismo naukowe
Tytuł :
DeepCryoPicker: fully automated deep neural network for single protein particle picking in cryo-EM.
Autorzy :
Al-Azzawi A; Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, 65211, USA.
Ouadou A; Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, 65211, USA.
Max H; Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, 65211, USA.
Duan Y; Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, 65211, USA.
Tanner JJ; Departments of Biochemistry and Chemistry, University of Missouri, Columbia, MO, 65211-2060, USA.
Cheng J; Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO, 65211, USA. .; Informatics Institute, University of Missouri, Columbia, MO, 65211, USA. .
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Źródło :
BMC bioinformatics [BMC Bioinformatics] 2020 Nov 09; Vol. 21 (1), pp. 509. Date of Electronic Publication: 2020 Nov 09.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Cryoelectron Microscopy/*methods
Proteins/*chemistry
Automation ; Cluster Analysis
Czasopismo naukowe
Tytuł :
Fully automated quantification of left ventricular volumes and function in cardiac MRI: clinical evaluation of a deep learning-based algorithm.
Autorzy :
Böttcher B; Institute of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Centre Rostock, Ernst-Heydemann-Str. 6, 18057, Rostock, Germany.
Beller E; Institute of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Centre Rostock, Ernst-Heydemann-Str. 6, 18057, Rostock, Germany.
Busse A; Institute of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Centre Rostock, Ernst-Heydemann-Str. 6, 18057, Rostock, Germany.
Cantré D; Institute of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Centre Rostock, Ernst-Heydemann-Str. 6, 18057, Rostock, Germany.
Yücel S; Department of Internal Medicine, Divison of Cardiology, University Medical Center Rostock, Rostock, Germany.
Öner A; Department of Internal Medicine, Divison of Cardiology, University Medical Center Rostock, Rostock, Germany.
Ince H; Department of Internal Medicine, Divison of Cardiology, University Medical Center Rostock, Rostock, Germany.
Weber MA; Institute of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Centre Rostock, Ernst-Heydemann-Str. 6, 18057, Rostock, Germany.
Meinel FG; Institute of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Centre Rostock, Ernst-Heydemann-Str. 6, 18057, Rostock, Germany. .
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Źródło :
The international journal of cardiovascular imaging [Int J Cardiovasc Imaging] 2020 Nov; Vol. 36 (11), pp. 2239-2247. Date of Electronic Publication: 2020 Jul 16.
Typ publikacji :
Comparative Study; Journal Article
MeSH Terms :
Deep Learning*
Diagnosis, Computer-Assisted*
Image Interpretation, Computer-Assisted*
Magnetic Resonance Imaging, Cine*
Ventricular Function, Left*
Heart Diseases/*diagnostic imaging
Heart Ventricles/*diagnostic imaging
Adolescent ; Adult ; Aged ; Aged, 80 and over ; Automation ; Feasibility Studies ; Female ; Heart Diseases/physiopathology ; Heart Ventricles/physiopathology ; Humans ; Male ; Middle Aged ; Predictive Value of Tests ; Reproducibility of Results ; Retrospective Studies ; Young Adult
Czasopismo naukowe
Tytuł :
Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays.
Autorzy :
Brunese L; Department of Medicine and Health Sciences 'Vincenzo Tiberio', University of Molise, Campobasso, Italy.
Mercaldo F; Department of Medicine and Health Sciences 'Vincenzo Tiberio', University of Molise, Campobasso, Italy; Institute for Informatics and Telematics, National Research Council of Italy (CNR), Pisa, Italy.
Reginelli A; Department of Precision Medicine, University of Campania 'Luigi Vanvitelli', Napoli, Italy.
Santone A; Department of Biosciences and Territory, University of Molise, Pesche (IS), Italy.
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Źródło :
Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2020 Nov; Vol. 196, pp. 105608. Date of Electronic Publication: 2020 Jun 20.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Coronavirus Infections/*diagnostic imaging
Pneumonia, Viral/*diagnostic imaging
Radiography, Thoracic/*methods
Algorithms ; Betacoronavirus ; Humans ; Image Processing, Computer-Assisted/methods ; Lung Diseases/diagnostic imaging ; Neural Networks, Computer ; Pandemics ; Radiographic Image Interpretation, Computer-Assisted/methods ; Reproducibility of Results ; X-Rays
SCR Disease Name :
COVID-19
Czasopismo naukowe
Tytuł :
Implementation of a Deep Learning-Based Computer-Aided Detection System for the Interpretation of Chest Radiographs in Patients Suspected for COVID-19.
Autorzy :
Hwang EJ; Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
Kim H; Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
Yoon SH; Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
Goo JM; Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
Park CM; Department of Radiology, Seoul National University College of Medicine, Seoul, Korea. .
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Źródło :
Korean journal of radiology [Korean J Radiol] 2020 Oct; Vol. 21 (10), pp. 1150-1160. Date of Electronic Publication: 2020 Jul 17.
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms :
Betacoronavirus*
Deep Learning*
Radiography, Thoracic*/methods
Coronavirus Infections/*diagnostic imaging
Pneumonia, Viral/*diagnostic imaging
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Pandemics ; Retrospective Studies ; Tomography, X-Ray Computed/methods
SCR Disease Name :
COVID-19
Czasopismo naukowe
Tytuł :
Towards explainable deep neural networks (xDNN).
Autorzy :
Angelov P; School of Computing and Communications, LIRA Research Centre, Lancaster University, Lancaster, LA1 4WA, UK. Electronic address: .
Soares E; School of Computing and Communications, LIRA Research Centre, Lancaster University, Lancaster, LA1 4WA, UK. Electronic address: .
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Źródło :
Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2020 Oct; Vol. 130, pp. 185-194. Date of Electronic Publication: 2020 Jul 11.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Coronavirus Infections/*diagnostic imaging
Image Processing, Computer-Assisted/*methods
Pneumonia, Viral/*diagnostic imaging
Tomography, X-Ray Computed/*methods
Humans ; Pandemics
SCR Disease Name :
COVID-19
Czasopismo naukowe
Tytuł :
Deep Learning Based Software to Identify Large Vessel Occlusion on Noncontrast Computed Tomography.
Autorzy :
Olive-Gadea M; Stroke Unit, Neurology Department, Hospital Vall d'Hebron, Departament de Medicina, Universitat Autònoma de Barcelona (M.O.-G., M.R.).
Crespo C; Methinks Software, Barcelona, Spain (C.C., C.G., C.M.).
Granes C; Methinks Software, Barcelona, Spain (C.C., C.G., C.M.).
Hernandez-Perez M; Stroke Unit, Hospital Germans Trias i Pujol, Badalona, Spain (M.H.-P., N.P.d.l.O.).
Pérez de la Ossa N; Stroke Unit, Hospital Germans Trias i Pujol, Badalona, Spain (M.H.-P., N.P.d.l.O.).
Laredo C; Comprehensive Stroke Center, Hospital Clínic, Barcelona, Spain (C.L., X.U.).
Urra X; Comprehensive Stroke Center, Hospital Clínic, Barcelona, Spain (C.L., X.U.).
Carlos Soler J; Radiology Department, Hospital Clínic, Barcelona, Spain (J.C.S., A.S.).
Soler A; Radiology Department, Hospital Clínic, Barcelona, Spain (J.C.S., A.S.).
Puyalto P; Radiology Department, Hospital Germans Trias i Pujol, Badalona, Spain (P.P., P.C.).
Cuadras P; Radiology Department, Hospital Germans Trias i Pujol, Badalona, Spain (P.P., P.C.).; Universitat Internacional de Catalunya, Faculty of Medicine and Health Science, Medicine Department, Sant Cugat del Vallès, Spain (P.C.).
Marti C; Methinks Software, Barcelona, Spain (C.C., C.G., C.M.).
Ribo M; Stroke Unit, Neurology Department, Hospital Vall d'Hebron, Departament de Medicina, Universitat Autònoma de Barcelona (M.O.-G., M.R.).
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Źródło :
Stroke [Stroke] 2020 Oct; Vol. 51 (10), pp. 3133-3137. Date of Electronic Publication: 2020 Aug 26.
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms :
Deep Learning*
Brain Ischemia/*diagnostic imaging
Infarction, Middle Cerebral Artery/*diagnostic imaging
Stroke/*diagnostic imaging
Computed Tomography Angiography ; Databases, Factual ; Humans ; Middle Cerebral Artery/diagnostic imaging ; Sensitivity and Specificity ; Software ; Tomography, X-Ray Computed
Czasopismo naukowe
Tytuł :
Deep learning for predicting the occurrence of cardiopulmonary diseases in Nanjing, China.
Autorzy :
Wang C; School of Energy and Environment, Southeast University, Nanjing, 210096, China; State Key Laboratory of Environmental Medicine Engineering, Ministry of Education, Southeast University, Nanjing, 210096, PR China. Electronic address: .
Qi Y; School of Architecture and Urban Planning, Nanjing University, No. 22, Hankoulu Road, Nanjing, 210093, PR China. Electronic address: .
Zhu G; School of Energy and Environment, Southeast University, Nanjing, 210096, China; State Key Laboratory of Environmental Medicine Engineering, Ministry of Education, Southeast University, Nanjing, 210096, PR China. Electronic address: .
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Źródło :
Chemosphere [Chemosphere] 2020 Oct; Vol. 257, pp. 127176. Date of Electronic Publication: 2020 May 27.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Air Pollution/*statistics & numerical data
Cardiovascular Diseases/*epidemiology
Air Pollutants/analysis ; Air Pollution/analysis ; China/epidemiology ; Humans ; Incidence ; Nitrogen Dioxide/analysis ; Particulate Matter/analysis ; Seasons
Czasopismo naukowe
Tytuł :
Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning.
Autorzy :
Minaee S; Snap Inc., Seattle, WA, USA. Electronic address: .
Kafieh R; Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Iran. Electronic address: .
Sonka M; Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, USA.
Yazdani S; ECE Department, Isfahan University of Technology, Iran.
Jamalipour Soufi G; Radiology Department, Isfahan University of Medical Sciences, Isfahan, Iran.
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Źródło :
Medical image analysis [Med Image Anal] 2020 Oct; Vol. 65, pp. 101794. Date of Electronic Publication: 2020 Jul 21.
Typ publikacji :
Journal Article; Research Support, N.I.H., Extramural
MeSH Terms :
Datasets as Topic*
Deep Learning*
Radiographic Image Interpretation, Computer-Assisted*
Radiography, Thoracic*
Coronavirus Infections/*diagnostic imaging
Pneumonia, Viral/*diagnostic imaging
Betacoronavirus ; Data Interpretation, Statistical ; Diagnosis, Differential ; Humans ; Neural Networks, Computer ; Pandemics ; Predictive Value of Tests ; Sensitivity and Specificity
SCR Disease Name :
COVID-19
Czasopismo naukowe
Tytuł :
An ensemble approach for CircRNA-disease association prediction based on autoencoder and deep neural network.
Autorzy :
Deepthi K; Bioinformatics Lab, Department of Computer Science, Cochin University of Science and Technology, Kochi 682022, Kerala, India; Department of Computer Science, College of Engineering, Vadakara, Kozhikkode 673104, Kerala, India. Electronic address: .
Jereesh AS; Bioinformatics Lab, Department of Computer Science, Cochin University of Science and Technology, Kochi 682022, Kerala, India. Electronic address: .
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Źródło :
Gene [Gene] 2020 Dec 15; Vol. 762, pp. 145040. Date of Electronic Publication: 2020 Aug 07.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Genetic Predisposition to Disease*
RNA, Circular/*genetics
Genome-Wide Association Study/methods ; Genomics/methods ; Humans ; RNA, Circular/metabolism
Czasopismo naukowe
Tytuł :
COVID19XrayNet: A Two-Step Transfer Learning Model for the COVID-19 Detecting Problem Based on a Limited Number of Chest X-Ray Images.
Autorzy :
Zhang R; BioKnow Health Informatics Lab, College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China.
Guo Z; School of Computing and Information, University of Pittsburgh, 135 N Bellefield Ave, Pittsburgh, PA, 15213, USA.
Sun Y; School of Computing and Information, University of Pittsburgh, 135 N Bellefield Ave, Pittsburgh, PA, 15213, USA.
Lu Q; School of Computing and Information, University of Pittsburgh, 135 N Bellefield Ave, Pittsburgh, PA, 15213, USA.
Xu Z; School of Computing and Information, University of Pittsburgh, 135 N Bellefield Ave, Pittsburgh, PA, 15213, USA.
Yao Z; BioKnow Health Informatics Lab, College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China.
Duan M; BioKnow Health Informatics Lab, College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China.
Liu S; BioKnow Health Informatics Lab, College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China.
Ren Y; College of Information Technology, Jilin Agricultural University, Changchun, 130118, Jilin, China.
Huang L; BioKnow Health Informatics Lab, College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China.
Zhou F; BioKnow Health Informatics Lab, College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China. .
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Źródło :
Interdisciplinary sciences, computational life sciences [Interdiscip Sci] 2020 Dec; Vol. 12 (4), pp. 555-565. Date of Electronic Publication: 2020 Sep 21.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Models, Biological*
Neural Networks, Computer*
X-Rays*
Clinical Laboratory Techniques/*methods
Coronavirus Infections/*diagnosis
Lung/*diagnostic imaging
Pneumonia, Viral/*diagnosis
Algorithms ; Betacoronavirus ; Coronavirus ; Coronavirus Infections/complications ; Coronavirus Infections/diagnostic imaging ; Coronavirus Infections/virology ; Databases, Factual ; Datasets as Topic ; Humans ; Machine Learning ; Pandemics ; Pneumonia/diagnosis ; Pneumonia/diagnostic imaging ; Pneumonia/etiology ; Pneumonia/virology ; Pneumonia, Viral/complications ; Pneumonia, Viral/diagnostic imaging ; Pneumonia, Viral/virology ; Radiography/methods ; Reference Values ; Tomography, X-Ray Computed/methods
SCR Disease Name :
COVID-19
Czasopismo naukowe
Tytuł :
Automatic diagnosis for cysts and tumors of both jaws on panoramic radiographs using a deep convolution neural network.
Autorzy :
Kwon O; Department of Oral and Maxillofacial Radiology, School of Dentistry, Seoul National University, Seoul, South Korea.
Yong TH; Department of Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea.
Kang SR; Department of Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea.
Kim JE; Department of Oral and Maxillofacial Radiology, Seoul National University Dental Hospital, Seoul, South Korea.
Huh KH; Department of Oral and Maxillofacial Radiology, School of Dentistry and Dental Research Institute, BK21, Seoul National University, Seoul, South Korea.
Heo MS; Department of Oral and Maxillofacial Radiology, School of Dentistry and Dental Research Institute, BK21, Seoul National University, Seoul, South Korea.
Lee SS; Department of Oral and Maxillofacial Radiology, School of Dentistry and Dental Research Institute, BK21, Seoul National University, Seoul, South Korea.
Choi SC; Department of Oral and Maxillofacial Radiology, School of Dentistry and Dental Research Institute, BK21, Seoul National University, Seoul, South Korea.
Yi WJ; Department of Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea.; Department of Oral and Maxillofacial Radiology, School of Dentistry and Dental Research Institute, BK21, Seoul National University, Seoul, South Korea.
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Źródło :
Dento maxillo facial radiology [Dentomaxillofac Radiol] 2020 Dec 01; Vol. 49 (8), pp. 20200185. Date of Electronic Publication: 2020 Jul 03.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Odontogenic Cysts*/diagnostic imaging
Area Under Curve ; Humans ; Neural Networks, Computer ; Radiography, Panoramic
Czasopismo naukowe
Tytuł :
Comparison of deep learning with regression analysis in creating predictive models for SARS-CoV-2 outcomes.
Autorzy :
Abdulaal A; Chelsea and Westminster NHS Foundation Trust, 369 Fulham Road, London, SW10 9NH, UK.
Patel A; Chelsea and Westminster NHS Foundation Trust, 369 Fulham Road, London, SW10 9NH, UK.
Charani E; National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK.
Denny S; Chelsea and Westminster NHS Foundation Trust, 369 Fulham Road, London, SW10 9NH, UK.
Alqahtani SA; King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia.; Johns Hopkins University, Baltimore, MD, USA.
Davies GW; Chelsea and Westminster NHS Foundation Trust, 369 Fulham Road, London, SW10 9NH, UK.
Mughal N; Chelsea and Westminster NHS Foundation Trust, 369 Fulham Road, London, SW10 9NH, UK.; National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK.; North West London Pathology, Imperial College Healthcare NHS Trust, Fulham Palace Road, London, W6 8RF, UK.
Moore LSP; Chelsea and Westminster NHS Foundation Trust, 369 Fulham Road, London, SW10 9NH, UK. .; National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK. .; North West London Pathology, Imperial College Healthcare NHS Trust, Fulham Palace Road, London, W6 8RF, UK. .
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Źródło :
BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2020 Nov 19; Vol. 20 (1), pp. 299. Date of Electronic Publication: 2020 Nov 19.
Typ publikacji :
Comparative Study; Journal Article
MeSH Terms :
Coronavirus Infections*
Deep Learning*
Pandemics*
Pneumonia, Viral*
Algorithms ; Betacoronavirus ; Female ; Humans ; London ; Male ; Middle Aged ; Models, Theoretical ; Neural Networks, Computer ; Proportional Hazards Models
SCR Disease Name :
COVID-19
Czasopismo naukowe
Tytuł :
Analyzing inter-reader variability affecting deep ensemble learning for COVID-19 detection in chest radiographs.
Autorzy :
Rajaraman S; Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, Maryland, United States of America.
Sornapudi S; Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, Missouri, United States of America.
Alderson PO; School of Medicine, Saint Louis University, St. Louis, Missouri, United States of America.
Folio LR; Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, Maryland, United States of America.
Antani SK; Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, Maryland, United States of America.
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Źródło :
PloS one [PLoS One] 2020 Nov 12; Vol. 15 (11), pp. e0242301. Date of Electronic Publication: 2020 Nov 12 (Print Publication: 2020).
Typ publikacji :
Journal Article; Research Support, N.I.H., Intramural
MeSH Terms :
Deep Learning*
Observer Variation*
Coronavirus Infections/*diagnostic imaging
Image Processing, Computer-Assisted/*methods
Pneumonia, Viral/*diagnostic imaging
Radiography, Thoracic/*standards
Algorithms ; Betacoronavirus ; Humans ; Neural Networks, Computer ; Pandemics
SCR Disease Name :
COVID-19
Czasopismo naukowe
Tytuł :
Capturing human categorization of natural images by combining deep networks and cognitive models.
Autorzy :
Battleday RM; Department of Computer Science, Princeton University, 35 Olden Street, Princeton, New Jersey, 08540, USA. .
Peterson JC; Department of Computer Science, Princeton University, 35 Olden Street, Princeton, New Jersey, 08540, USA. .
Griffiths TL; Department of Computer Science, Princeton University, 35 Olden Street, Princeton, New Jersey, 08540, USA.; Department of Psychology, Princeton University, South Drive, Princeton, New Jersey, 08540, USA.
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Źródło :
Nature communications [Nat Commun] 2020 Oct 27; Vol. 11 (1), pp. 5418. Date of Electronic Publication: 2020 Oct 27.
Typ publikacji :
Journal Article; Research Support, U.S. Gov't, Non-P.H.S.
MeSH Terms :
Cognition*
Deep Learning*
Pattern Recognition, Visual*
Decision Making ; Humans ; Judgment ; Memory ; Models, Psychological ; Visual Cortex
Czasopismo naukowe
Tytuł :
Learning Credit Assignment.
Autorzy :
Li C; PMI Lab, School of Physics, Sun Yat-sen University, Guangzhou 510275, People's Republic of China.
Huang H; PMI Lab, School of Physics, Sun Yat-sen University, Guangzhou 510275, People's Republic of China.
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Źródło :
Physical review letters [Phys Rev Lett] 2020 Oct 23; Vol. 125 (17), pp. 178301.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Neural Networks, Computer*
Nonlinear Dynamics
Czasopismo naukowe
Tytuł :
Deep learning-assisted comparative analysis of animal trajectories with DeepHL.
Autorzy :
Maekawa T; Graduate School of Information Science and Technology, Osaka University, Osaka, Japan. .
Ohara K; Graduate School of Information Science and Technology, Osaka University, Osaka, Japan.
Zhang Y; Graduate School of Information Science and Technology, Osaka University, Osaka, Japan.
Fukutomi M; Graduate School of Life Science, Hokkaido University, Hokkaido, Japan.
Matsumoto S; Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan.
Matsumura K; Graduate School of Environmental and Life Science, Okayama University, Okayama, Japan.
Shidara H; Department of Biological Sciences, Hokkaido University, Hokkaido, Japan.
Yamazaki SJ; Graduate School of Science, Osaka University, Osaka, Japan.
Fujisawa R; Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Japan.
Ide K; Graduate School of Brain Science, Doshisha University, Kyotanabe, Japan.
Nagaya N; Department of Intelligent Systems, Kyoto Sangyo University, Kyoto, Japan.
Yamazaki K; Department of Forest Science, Tokyo University of Agriculture, Tokyo, Japan.
Koike S; Graduate School of Agriculture, Tokyo University of Agriculture and Technology, Tokyo, Japan.
Miyatake T; Graduate School of Environmental and Life Science, Okayama University, Okayama, Japan.
Kimura KD; Graduate School of Science, Osaka University, Osaka, Japan.; Graduate School of Science, Nagoya City University, Nagoya, Japan.
Ogawa H; Department of Biological Sciences, Hokkaido University, Hokkaido, Japan.
Takahashi S; Graduate School of Brain Science, Doshisha University, Kyotanabe, Japan.
Yoda K; Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan.
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Źródło :
Nature communications [Nat Commun] 2020 Oct 20; Vol. 11 (1), pp. 5316. Date of Electronic Publication: 2020 Oct 20.
Typ publikacji :
Comparative Study; Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms :
Deep Learning*
Birds/*physiology
Insecta/*physiology
Mice/*physiology
Ursidae/*physiology
Animals ; Behavior, Animal ; Female ; Movement ; Neural Networks, Computer ; Software
Czasopismo naukowe
Tytuł :
Non-invasive decision support for NSCLC treatment using PET/CT radiomics.
Autorzy :
Mu W; Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
Jiang L; Department of Nuclear Medicine, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Zhang J; Department of Nuclear Medicine, the Fourth Hospital of Hebei Medical University, Hebei, China.; Department of Nuclear Medicine, Baoding No.1 Central Hospital, Baoding, Hebei, China.
Shi Y; Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
Gray JE; Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
Tunali I; Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
Gao C; NHC and CAMS Key Laboratory of Molecular Probe and Targeted Theranostics, Molecular Imaging Research Center (MIRC), Harbin Medical University, Harbin, Heilongjiang, China.; TOF-PET/CT/MR center, the Fourth Hospital of Harbin Medical University, Harbin Medical University, Harbin, Heilongjiang, China.
Sun Y; NHC and CAMS Key Laboratory of Molecular Probe and Targeted Theranostics, Molecular Imaging Research Center (MIRC), Harbin Medical University, Harbin, Heilongjiang, China.; TOF-PET/CT/MR center, the Fourth Hospital of Harbin Medical University, Harbin Medical University, Harbin, Heilongjiang, China.
Tian J; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China.; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Zhao X; Department of Nuclear Medicine, the Fourth Hospital of Hebei Medical University, Hebei, China. .
Sun X; NHC and CAMS Key Laboratory of Molecular Probe and Targeted Theranostics, Molecular Imaging Research Center (MIRC), Harbin Medical University, Harbin, Heilongjiang, China. .; TOF-PET/CT/MR center, the Fourth Hospital of Harbin Medical University, Harbin Medical University, Harbin, Heilongjiang, China. .
Gillies RJ; Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. .
Schabath MB; Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. .; Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. .
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Źródło :
Nature communications [Nat Commun] 2020 Oct 16; Vol. 11 (1), pp. 5228. Date of Electronic Publication: 2020 Oct 16.
Typ publikacji :
Journal Article; Multicenter Study; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
MeSH Terms :
Deep Learning*
Carcinoma, Non-Small-Cell Lung/*diagnostic imaging
Lung Neoplasms/*diagnostic imaging
Protein Kinase Inhibitors/*administration & dosage
Aged ; Carcinoma, Non-Small-Cell Lung/drug therapy ; Carcinoma, Non-Small-Cell Lung/genetics ; Carcinoma, Non-Small-Cell Lung/mortality ; ErbB Receptors/genetics ; ErbB Receptors/metabolism ; Female ; Humans ; Lung Neoplasms/drug therapy ; Lung Neoplasms/genetics ; Lung Neoplasms/mortality ; Male ; Middle Aged ; Mutation ; Positron Emission Tomography Computed Tomography ; Progression-Free Survival
Czasopismo naukowe

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