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


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Tytuł :
Artificial intelligence-based hybrid deep learning models for image classification: The first narrative review.
Autorzy :
Jena B; Department of CSE, International Institute of Information Technology, Bhubaneswar, India.
Saxena S; Department of CSE, International Institute of Information Technology, Bhubaneswar, India.
Nayak GK; Department of CSE, International Institute of Information Technology, Bhubaneswar, India.
Saba L; Department of Radiology, University of Cagliari, Italy.
Sharma N; School of Biomedical Engineering, IIT (BHU), Varanasi, India.
Suri JS; Stroke Diagnosis and Monitoring Division, AtheroPoint™, Roseville, CA, USA. Electronic address: .
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Źródło :
Computers in biology and medicine [Comput Biol Med] 2021 Oct; Vol. 137, pp. 104803. Date of Electronic Publication: 2021 Aug 27.
Typ publikacji :
Journal Article; Review
MeSH Terms :
Artificial Intelligence*
Deep Learning*
Diagnostic Imaging ; Machine Learning
Czasopismo naukowe
Tytuł :
End-to-end deep learning nonrigid motion-corrected reconstruction for highly accelerated free-breathing coronary MRA.
Autorzy :
Qi H; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.; School of Biomedical Engineering, ShanghaiTech University, Shanghai, People's Republic of China.
Hajhosseiny R; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Cruz G; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Kuestner T; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.; Medical Image and Data Analysis, Department of Interventional and Diagnostic Radiology, University Hospital of Tübingen, Tübingen, Germany.
Kunze K; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.; MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom.
Neji R; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.; MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom.
Botnar R; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.; Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile.
Prieto C; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.; Escuela de Ingeniería, Pontificia Universidad Católica de Chile, Santiago, Chile.
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Źródło :
Magnetic resonance in medicine [Magn Reson Med] 2021 Oct; Vol. 86 (4), pp. 1983-1996. Date of Electronic Publication: 2021 Jun 06.
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms :
Deep Learning*
Magnetic Resonance Angiography*
Heart ; Image Processing, Computer-Assisted ; Imaging, Three-Dimensional ; Motion ; Retrospective Studies
Czasopismo naukowe
Tytuł :
Optimal ECG-lead selection increases generalizability of deep learning on ECG abnormality classification.
Autorzy :
Lai C; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.; Alliance for Cardiovascular Diagnostic and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, Baltimore, MD 21218, USA.
Zhou S; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.; Alliance for Cardiovascular Diagnostic and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, Baltimore, MD 21218, USA.
Trayanova NA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.; Alliance for Cardiovascular Diagnostic and Treatment Innovation, Whiting School of Engineering and School of Medicine, Johns Hopkins University, Baltimore, MD 21218, USA.
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Źródło :
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences [Philos Trans A Math Phys Eng Sci] 2021 Dec 13; Vol. 379 (2212), pp. 20200258. Date of Electronic Publication: 2021 Oct 25.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Algorithms ; Electrocardiography
Czasopismo naukowe
Tytuł :
Effective End-to-End Deep Learning Process in Medical Imaging Using Independent Task Learning: Application for Diagnosis of Maxillary Sinusitis.
Autorzy :
Oh JH; Department of Biomedical Science and Technology, Graduate School, Kyung Hee University, Seoul, Korea.
Kim HG; Department of Radiology, Kyung Hee University College of Medicine, Kyung Hee University Hospital, Seoul, Korea.
Lee KM; Department of Radiology, Kyung Hee University College of Medicine, Kyung Hee University Hospital, Seoul, Korea. .
Ryu CW; Department of Radiology, Kyung Hee University College of Medicine, Kyung Hee University Hospital at Gangdong, Seoul, Korea.
Park S; Department of Radiology, Kyung Hee University College of Medicine, Kyung Hee University Hospital at Gangdong, Seoul, Korea.
Jang JH; Department of Radiology, Korea Cancer Center Hospital, Seoul, Korea.
Choi HS; Department of Radiology, Seoul Medical Center, Seoul, Korea.
Kim EJ; Department of Radiology, Kyung Hee University College of Medicine, Kyung Hee University Hospital, Seoul, Korea.
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Źródło :
Yonsei medical journal [Yonsei Med J] 2021 Dec; Vol. 62 (12), pp. 1125-1135.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Maxillary Sinusitis*/diagnostic imaging
Humans ; ROC Curve ; Radiography ; Retrospective Studies
Czasopismo naukowe
Tytuł :
Fully automated deep learning-based localization and segmentation of the locus coeruleus in aging and Parkinson's disease using neuromelanin-sensitive MRI.
Autorzy :
Dünnwald M; Department of Neurology, Faculty of Medicine, Otto von Guericke University Magdeburg (OVGU), Magdeburg, Germany. .; Faculty of Computer Science, OVGU, Magdeburg, Germany. .
Ernst P; Faculty of Computer Science, OVGU, Magdeburg, Germany.
Düzel E; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.; Institute of Cognitive Neurology and Dementia Research (IKND), Faculty of Medicine, OVGU, Magdeburg, Germany.; Institute of Cognitive Neuroscience, University College London, London, Great Britain, UK.; Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany.
Tönnies K; Faculty of Computer Science, OVGU, Magdeburg, Germany.
Betts MJ; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.; Institute of Cognitive Neurology and Dementia Research (IKND), Faculty of Medicine, OVGU, Magdeburg, Germany.; Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany.
Oeltze-Jafra S; Department of Neurology, Faculty of Medicine, Otto von Guericke University Magdeburg (OVGU), Magdeburg, Germany.; Faculty of Computer Science, OVGU, Magdeburg, Germany.; Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany.
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Źródło :
International journal of computer assisted radiology and surgery [Int J Comput Assist Radiol Surg] 2021 Dec; Vol. 16 (12), pp. 2129-2135. Date of Electronic Publication: 2021 Nov 19.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Parkinson Disease*/diagnostic imaging
Humans ; Image Processing, Computer-Assisted ; Locus Coeruleus ; Magnetic Resonance Imaging ; Melanins
Czasopismo naukowe
Tytuł :
Liver fibrosis staging by deep learning: a visual-based explanation of diagnostic decisions of the model.
Autorzy :
Yin Y; Department of Radiology, Medical Imaging Center Groningen, University of Groningen, University Medical Center Groningen, PO Box 30001, 9700 RB, Groningen, The Netherlands.
Yakar D; Department of Radiology, Medical Imaging Center Groningen, University of Groningen, University Medical Center Groningen, PO Box 30001, 9700 RB, Groningen, The Netherlands.
Dierckx RAJO; Department of Radiology, Medical Imaging Center Groningen, University of Groningen, University Medical Center Groningen, PO Box 30001, 9700 RB, Groningen, The Netherlands.
Mouridsen KB; Department of Radiology, Medical Imaging Center Groningen, University of Groningen, University Medical Center Groningen, PO Box 30001, 9700 RB, Groningen, The Netherlands.; Department of Clinical Medicine - Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark.
Kwee TC; Department of Radiology, Medical Imaging Center Groningen, University of Groningen, University Medical Center Groningen, PO Box 30001, 9700 RB, Groningen, The Netherlands.
de Haas RJ; Department of Radiology, Medical Imaging Center Groningen, University of Groningen, University Medical Center Groningen, PO Box 30001, 9700 RB, Groningen, The Netherlands. .
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Źródło :
European radiology [Eur Radiol] 2021 Dec; Vol. 31 (12), pp. 9620-9627. Date of Electronic Publication: 2021 May 20.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Humans ; Liver/pathology ; Liver Cirrhosis/diagnostic imaging ; Liver Cirrhosis/pathology ; Retrospective Studies ; Spleen
Czasopismo naukowe
Tytuł :
Delayed brain development of Rolandic epilepsy profiled by deep learning-based neuroanatomic imaging.
Autorzy :
Zhang Q; Department of Medical Imaging, Jinling Hospital, the First School of Clinical Medicine, Southern Medical University, Nanjing, China.; Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.
He Y; Department of Neurology, Children's Hospital of Nanjing Medical University, Nanjing, China.
Qu T; Deepwise AI Lab, Deepwise Inc., Beijing, China.
Yang F; Department of Neurology, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.
Lin Y; Department of Neurology, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.
Hu Z; Department of Neurology, Children's Hospital of Nanjing Medical University, Nanjing, China.
Li X; Deepwise AI Lab, Deepwise Inc., Beijing, China.
Xu Q; Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.; College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
Xing W; Department of Radiology, Third Affiliated Hospital of Soochow University & Changzhou First People's Hospital, Changzhou, China.
Gumenyuk V; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Stufflebeam SM; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Liu H; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Lu G; Department of Medical Imaging, Jinling Hospital, the First School of Clinical Medicine, Southern Medical University, Nanjing, China. .; Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China. .
Zhang Z; Department of Medical Imaging, Jinling Hospital, the First School of Clinical Medicine, Southern Medical University, Nanjing, China. .; Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China. .
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Źródło :
European radiology [Eur Radiol] 2021 Dec; Vol. 31 (12), pp. 9628-9637. Date of Electronic Publication: 2021 May 20.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Epilepsy, Rolandic*/diagnostic imaging
Brain/diagnostic imaging ; Electroencephalography ; Humans ; Magnetic Resonance Imaging
Czasopismo naukowe
Tytuł :
Deep learning based spectral CT imaging.
Autorzy :
Wu W; Department of Diagnostic Radiology, Queen Mary Hospital, University of Hong Kong, Hong Kong, People's Republic of China; Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, School of Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.
Hu D; The Laboratory of Image Science and Technology, Southeast University, Nanjing, People's Republic of China.
Niu C; Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, School of Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.
Broeke LV; Department of Diagnostic Radiology, Queen Mary Hospital, University of Hong Kong, Hong Kong, People's Republic of China.
Butler APH; Department of Radiology, University of Otago, Christchurch, New Zealand.
Cao P; Department of Diagnostic Radiology, Queen Mary Hospital, University of Hong Kong, Hong Kong, People's Republic of China.
Atlas J; Department of Radiology, University of Otago, Christchurch, New Zealand.
Chernoglazov A; Department of Radiology, University of Otago, Christchurch, New Zealand.
Vardhanabhuti V; Department of Diagnostic Radiology, Queen Mary Hospital, University of Hong Kong, Hong Kong, People's Republic of China. Electronic address: .
Wang G; Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, School of Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.
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Źródło :
Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2021 Dec; Vol. 144, pp. 342-358. Date of Electronic Publication: 2021 Aug 28.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Algorithms ; Image Processing, Computer-Assisted ; Phantoms, Imaging ; Tomography, X-Ray Computed
Czasopismo naukowe
Tytuł :
A Deep Learning Approach to Segment and Classify C-Shaped Canal Morphologies in Mandibular Second Molars Using Cone-beam Computed Tomography.
Autorzy :
Sherwood AA; Mahatma Montessori Matriculation Higher Secondary School, Madurai, Tamil Nadu, India.
Sherwood AI; Department of Conservative Dentistry and Endodontics, CSI College of Dental Sciences, Madurai, Tamil Nadu, India. Electronic address: .
Setzer FC; Department of Endodontics, School of Dental Medicine, University of Pennsylvania, Philadelphia, Pennsylvania. Electronic address: .
K SD; Mahatma Montessori Matriculation Higher Secondary School, Madurai, Tamil Nadu, India.
Shamili JV; Department of Conservative Dentistry and Endodontics, CSI College of Dental Sciences, Madurai, Tamil Nadu, India.
John C; Department of Computer Science, Hal Marcus College of Science and Engineering, University of West Florida, Pensacola, Florida.
Schwendicke F; Department of Oral Diagnostics, Charité - Universitätsmedizin Berlin, Berlin, Germany.
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Źródło :
Journal of endodontics [J Endod] 2021 Dec; Vol. 47 (12), pp. 1907-1916. Date of Electronic Publication: 2021 Sep 24.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Tooth Root*
Cone-Beam Computed Tomography ; Dental Pulp Cavity ; Mandible/diagnostic imaging ; Molar/diagnostic imaging
Czasopismo naukowe
Tytuł :
Development of deep learning models for microglia analyses in brain tissue using DeePathology™ STUDIO.
Autorzy :
Möhle L; Department of Pathology, Translational Neurodegeneration Research and Neuropathology Lab, University of Oslo (UiO) and Oslo University Hospital (OUS), Oslo, Norway. Electronic address: .
Bascuñana P; Department of Pathology, Translational Neurodegeneration Research and Neuropathology Lab, University of Oslo (UiO) and Oslo University Hospital (OUS), Oslo, Norway.
Brackhan M; Department of Pathology, Translational Neurodegeneration Research and Neuropathology Lab, University of Oslo (UiO) and Oslo University Hospital (OUS), Oslo, Norway; LIED, University of Lübeck, Lübeck, Germany.
Pahnke J; Department of Pathology, Translational Neurodegeneration Research and Neuropathology Lab, University of Oslo (UiO) and Oslo University Hospital (OUS), Oslo, Norway; LIED, University of Lübeck, Lübeck, Germany; Department of Pharmacology, Faculty of Medicine, University of Latvia, Rīga, Latvia. Electronic address: .
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Źródło :
Journal of neuroscience methods [J Neurosci Methods] 2021 Dec 01; Vol. 364, pp. 109371. Date of Electronic Publication: 2021 Sep 27.
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms :
Deep Learning*
Animals ; Artificial Intelligence ; Brain ; Image Processing, Computer-Assisted ; Mice ; Microglia
Czasopismo naukowe
Tytuł :
Automatic pulmonary vessel segmentation on noncontrast chest CT: deep learning algorithm developed using spatiotemporally matched virtual noncontrast images and low-keV contrast-enhanced vessel maps.
Autorzy :
Nam JG; Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.; Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
Witanto JN; MedicalIp Co., Ltd., Seoul, 03127, Republic of Korea.
Park SJ; Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.; MedicalIp Co., Ltd., Seoul, 03127, Republic of Korea.
Yoo SJ; Department of Radiology, Hanyang University Medical Center and College of Medicine, Seoul, 04763, Republic of Korea.
Goo JM; Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.; Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
Yoon SH; Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. .; Seoul National University College of Medicine, Seoul, 03080, Republic of Korea. .
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Źródło :
European radiology [Eur Radiol] 2021 Dec; Vol. 31 (12), pp. 9012-9021. Date of Electronic Publication: 2021 May 19.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Algorithms ; Computed Tomography Angiography ; Humans ; Thorax ; Tomography, X-Ray Computed
Czasopismo naukowe
Tytuł :
Distinguishing nontuberculous mycobacteria from Mycobacterium tuberculosis lung disease from CT images using a deep learning framework.
Autorzy :
Wang L; TCM Key Research Laboratory for Infectious Disease Prevention for State Administration of Traditional Chinese Medicine, Tianjin Institute of Respiratory Diseases, Haihe Hospital, Tianjin University, Tianjin, People's Republic of China.
Ding W; TCM Key Research Laboratory for Infectious Disease Prevention for State Administration of Traditional Chinese Medicine, Tianjin Institute of Respiratory Diseases, Haihe Hospital, Tianjin University, Tianjin, People's Republic of China.
Mo Y; Deepwise AI Lab, Beijing Deepwise& League of PHD Technology Co., Ltd, Haidian District, 21st Floor, China Sinosteel Plaza, NO. 8, Haidian Avenue, Beijing, 100080, People's Republic of China.
Shi D; Deepwise AI Lab, Beijing Deepwise& League of PHD Technology Co., Ltd, Haidian District, 21st Floor, China Sinosteel Plaza, NO. 8, Haidian Avenue, Beijing, 100080, People's Republic of China.
Zhang S; TCM Key Research Laboratory for Infectious Disease Prevention for State Administration of Traditional Chinese Medicine, Tianjin Institute of Respiratory Diseases, Haihe Hospital, Tianjin University, Tianjin, People's Republic of China.
Zhong L; TCM Key Research Laboratory for Infectious Disease Prevention for State Administration of Traditional Chinese Medicine, Tianjin Institute of Respiratory Diseases, Haihe Hospital, Tianjin University, Tianjin, People's Republic of China.
Wang K; TCM Key Research Laboratory for Infectious Disease Prevention for State Administration of Traditional Chinese Medicine, Tianjin Institute of Respiratory Diseases, Haihe Hospital, Tianjin University, Tianjin, People's Republic of China.
Wang J; TCM Key Research Laboratory for Infectious Disease Prevention for State Administration of Traditional Chinese Medicine, Tianjin Institute of Respiratory Diseases, Haihe Hospital, Tianjin University, Tianjin, People's Republic of China.
Huang C; Deepwise AI Lab, Beijing Deepwise& League of PHD Technology Co., Ltd, Haidian District, 21st Floor, China Sinosteel Plaza, NO. 8, Haidian Avenue, Beijing, 100080, People's Republic of China.
Zhang S; Deepwise AI Lab, Beijing Deepwise& League of PHD Technology Co., Ltd, Haidian District, 21st Floor, China Sinosteel Plaza, NO. 8, Haidian Avenue, Beijing, 100080, People's Republic of China.
Ye Z; National Clinical Research Centre of Cancer, Key Laboratory of Cancer Prevention and Therapy, Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, People's Republic of China.
Shen J; TCM Key Research Laboratory for Infectious Disease Prevention for State Administration of Traditional Chinese Medicine, Tianjin Institute of Respiratory Diseases, Haihe Hospital, Tianjin University, Tianjin, People's Republic of China. .
Xing Z; TCM Key Research Laboratory for Infectious Disease Prevention for State Administration of Traditional Chinese Medicine, Tianjin Institute of Respiratory Diseases, Haihe Hospital, Tianjin University, Tianjin, People's Republic of China. .
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Źródło :
European journal of nuclear medicine and molecular imaging [Eur J Nucl Med Mol Imaging] 2021 Dec; Vol. 48 (13), pp. 4293-4306. Date of Electronic Publication: 2021 Jun 16.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Lung Diseases*/diagnostic imaging
Tuberculosis/*diagnostic imaging
Diagnosis, Differential ; Humans ; Mycobacterium tuberculosis ; Nontuberculous Mycobacteria ; Retrospective Studies ; Tomography, X-Ray Computed
Czasopismo naukowe
Tytuł :
Comparison of deep learning, radiomics and subjective assessment of chest CT findings in SARS-CoV-2 pneumonia.
Autorzy :
Arru C; Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA.
Ebrahimian S; Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA. Electronic address: .
Falaschi Z; Ospedale Maggiore della Carita', Novara, Italy.
Hansen JV; Department of Cardiology, Department of Clinical Medicine, Aarhus University Hospital, Palle Juul Jensens Boulevard 99, 8200 Aarhus N, Denmark. Electronic address: .
Pasche A; Ospedale Maggiore della Carita', Novara, Italy.
Lyhne MD; Department of Cardiology, Department of Clinical Medicine, Aarhus University Hospital, Palle Juul Jensens Boulevard 99, 8200 Aarhus N, Denmark. Electronic address: .
Zimmermann M; Siemens Healthcare GmbH, Diagnostic Imaging, Erlangen, Germany. Electronic address: .
Durlak F; Siemens Healthcare GmbH, Diagnostic Imaging, Erlangen, Germany. Electronic address: .
Mitschke M; Siemens Healthcare GmbH, Diagnostic Imaging, Erlangen, Germany. Electronic address: .
Carriero A; Ospedale Maggiore della Carita', Novara, Italy.
Nielsen-Kudsk JE; Department of Cardiology, Department of Clinical Medicine, Aarhus University Hospital, Palle Juul Jensens Boulevard 99, 8200 Aarhus N, Denmark.
Kalra MK; Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA. Electronic address: .
Saba L; Azienda Ospedaliera Universitaria di Cagliari, Cagliari, Italy.
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Źródło :
Clinical imaging [Clin Imaging] 2021 Dec; Vol. 80, pp. 58-66. Date of Electronic Publication: 2021 Jul 01.
Typ publikacji :
Journal Article; Multicenter Study
MeSH Terms :
COVID-19*
Deep Learning*
Aged ; Aged, 80 and over ; Humans ; Lung/diagnostic imaging ; Middle Aged ; Retrospective Studies ; SARS-CoV-2 ; Tomography, X-Ray Computed
Czasopismo naukowe
Tytuł :
Deep learning assistance increases the detection sensitivity of radiologists for secondary intracranial aneurysms in subarachnoid hemorrhage.
Autorzy :
Pennig L; Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Kerpener Straße 62, 50937, Cologne, Germany. .
Hoyer UCI; Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Kerpener Straße 62, 50937, Cologne, Germany.
Krauskopf A; Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Kerpener Straße 62, 50937, Cologne, Germany.; Department of Diagnostic and Interventional Radiology, University Hospital Düsseldorf, Düsseldorf, Germany.
Shahzad R; Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Kerpener Straße 62, 50937, Cologne, Germany.; Innovative Technologies, Philips Healthcare, Aachen, Germany.
Jünger ST; Center for Neurosurgery, Department of General Neurosurgery, University of Cologne, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany.
Thiele F; Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Kerpener Straße 62, 50937, Cologne, Germany.; Innovative Technologies, Philips Healthcare, Aachen, Germany.
Laukamp KR; Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Kerpener Straße 62, 50937, Cologne, Germany.
Grunz JP; Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany.
Perkuhn M; Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Kerpener Straße 62, 50937, Cologne, Germany.; Innovative Technologies, Philips Healthcare, Aachen, Germany.
Schlamann M; Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Kerpener Straße 62, 50937, Cologne, Germany.
Kabbasch C; Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Kerpener Straße 62, 50937, Cologne, Germany.
Borggrefe J; Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Kerpener Straße 62, 50937, Cologne, Germany.; Department of Radiology, Neuroradiology and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, Bochum, Germany.
Goertz L; Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Kerpener Straße 62, 50937, Cologne, Germany.; Center for Neurosurgery, Department of General Neurosurgery, University of Cologne, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany.
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Źródło :
Neuroradiology [Neuroradiology] 2021 Dec; Vol. 63 (12), pp. 1985-1994. Date of Electronic Publication: 2021 Apr 10.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Intracranial Aneurysm*/diagnostic imaging
Subarachnoid Hemorrhage*/diagnostic imaging
Angiography, Digital Subtraction ; Cerebral Angiography ; Humans ; Radiologists ; Sensitivity and Specificity
Czasopismo naukowe
Tytuł :
Differential diagnosis of benign and malignant vertebral fracture on CT using deep learning.
Autorzy :
Li Y; Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China.
Zhang Y; Department of Radiological Sciences, University of California, Irvine, CA, USA.
Zhang E; Department of Radiology, Peking University International Hospital, Beijing, People's Republic of China.
Chen Y; Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China.
Wang Q; Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China.
Liu K; Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China.
Yu HJ; Department of Radiological Sciences, University of California, Irvine, CA, USA.
Yuan H; Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China.
Lang N; Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China. .
Su MY; Department of Radiological Sciences, University of California, Irvine, CA, USA.
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Źródło :
European radiology [Eur Radiol] 2021 Dec; Vol. 31 (12), pp. 9612-9619. Date of Electronic Publication: 2021 May 16.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Spinal Fractures*/diagnostic imaging
Diagnosis, Differential ; Humans ; Retrospective Studies ; Tomography, X-Ray Computed
Czasopismo naukowe
Tytuł :
Application of deep learning to identify COVID-19 infection in posteroanterior chest X-rays.
Autorzy :
Maharjan J; Dascena, Inc., Houston, TX, United States. Electronic address: .
Calvert J; Dascena, Inc., Houston, TX, United States. Electronic address: .
Pellegrini E; Dascena, Inc., Houston, TX, United States. Electronic address: .
Green-Saxena A; Dascena, Inc., Houston, TX, United States. Electronic address: .
Hoffman J; Dascena, Inc., Houston, TX, United States. Electronic address: .
McCoy A; Cape Regional Medical Center, Cape May Court House, NJ, United States. Electronic address: .
Mao Q; Dascena, Inc., Houston, TX, United States. Electronic address: .
Das R; Dascena, Inc., Houston, TX, United States. Electronic address: .
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Źródło :
Clinical imaging [Clin Imaging] 2021 Dec; Vol. 80, pp. 268-273. Date of Electronic Publication: 2021 Jul 24.
Typ publikacji :
Journal Article
MeSH Terms :
COVID-19*
Deep Learning*
Humans ; Neural Networks, Computer ; SARS-CoV-2 ; X-Rays
Czasopismo naukowe
Tytuł :
Deep-Hook: A trusted deep learning-based framework for unknown malware detection and classification in Linux cloud environments.
Autorzy :
Landman T; Malware Lab, Cyber Security Research Center, Ben-Gurion University of the Negev, Israel; Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Israel.
Nissim N; Malware Lab, Cyber Security Research Center, Ben-Gurion University of the Negev, Israel; Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Israel. Electronic address: .
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Źródło :
Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2021 Dec; Vol. 144, pp. 648-685. Date of Electronic Publication: 2021 Oct 02.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Cloud Computing ; Neural Networks, Computer ; Software
Czasopismo naukowe
Tytuł :
Using deep learning and natural language processing models to detect child physical abuse.
Autorzy :
Shahi N; Division of Pediatric Surgery, Children's Hospital Colorado, Aurora, CO, USA; Department of Surgery, University of Colorado School of Medicine, Aurora, CO, USA; Department of Surgery, University of Massachusetts School of Medicine, Worcester, MA, USA. Electronic address: .
Shahi AK; Division of Pediatric Surgery, Children's Hospital Colorado, Aurora, CO, USA; Department of Surgery, University of Colorado School of Medicine, Aurora, CO, USA; Department of Surgery, University of Massachusetts School of Medicine, Worcester, MA, USA; Department of Emergency Medicine, University of Colorado School of Medicine, Aurora, CO, USA; Kempe Center for the Prevention and Treatment of Child Abuse and Neglect, University of Colorado School of Medicine, Aurora, CO, USA.
Phillips R; Division of Pediatric Surgery, Children's Hospital Colorado, Aurora, CO, USA; Department of Surgery, University of Colorado School of Medicine, Aurora, CO, USA.
Shirek G; Division of Pediatric Surgery, Children's Hospital Colorado, Aurora, CO, USA.
Lindberg DM; Department of Emergency Medicine, University of Colorado School of Medicine, Aurora, CO, USA; Kempe Center for the Prevention and Treatment of Child Abuse and Neglect, University of Colorado School of Medicine, Aurora, CO, USA.
Moulton SL; Division of Pediatric Surgery, Children's Hospital Colorado, Aurora, CO, USA; Department of Surgery, University of Colorado School of Medicine, Aurora, CO, USA.
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Źródło :
Journal of pediatric surgery [J Pediatr Surg] 2021 Dec; Vol. 56 (12), pp. 2326-2332. Date of Electronic Publication: 2021 Mar 18.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Natural Language Processing*
Child ; Electronic Health Records ; Humans ; Physical Abuse ; Radiography
Czasopismo naukowe
Tytuł :
Biologically motivated learning method for deep neural networks using hierarchical competitive learning.
Autorzy :
Shinozaki T; Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT), 1-4 Yamadaoka, Suita, Osaka 565-0871, Japan; Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan. Electronic address: .
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Źródło :
Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2021 Dec; Vol. 144, pp. 271-278. Date of Electronic Publication: 2021 Sep 03.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Neural Networks, Computer*
Machine Learning
Czasopismo naukowe
Tytuł :
Evaluating subscapularis tendon tears on axillary lateral radiographs using deep learning.
Autorzy :
Kang Y; Department of Radiology, Seoul National University Bundang Hospital, 82 Gumi-ro, 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea. .
Choi D; Department of Radiology, Seoul National University Bundang Hospital, 82 Gumi-ro, 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea.
Lee KJ; Department of Radiology, Seoul National University Bundang Hospital, 82 Gumi-ro, 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea. .
Oh JH; Department of Orthopedic Surgery, Seoul National University Bundang Hospital, 82 Gumi-ro, 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea.
Kim BR; Department of Radiology, Seoul National University Bundang Hospital, 82 Gumi-ro, 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea.
Ahn JM; Department of Radiology, Seoul National University Bundang Hospital, 82 Gumi-ro, 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea.
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Źródło :
European radiology [Eur Radiol] 2021 Dec; Vol. 31 (12), pp. 9408-9417. Date of Electronic Publication: 2021 May 20.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Rotator Cuff Injuries*/diagnostic imaging
Arthroscopy ; Humans ; Radiography ; Retrospective Studies ; Rotator Cuff
Czasopismo naukowe

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