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


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Tytuł :
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis.
Autorzy :
van der Velden BHM; Image Sciences Institute, University Medical Center Utrecht, Q.02.4.45, P.O. Box 85500, Utrecht, GA 3508, the Netherlands. Electronic address: .
Kuijf HJ; Image Sciences Institute, University Medical Center Utrecht, Q.02.4.45, P.O. Box 85500, Utrecht, GA 3508, the Netherlands.
Gilhuijs KGA; Image Sciences Institute, University Medical Center Utrecht, Q.02.4.45, P.O. Box 85500, Utrecht, GA 3508, the Netherlands.
Viergever MA; Image Sciences Institute, University Medical Center Utrecht, Q.02.4.45, P.O. Box 85500, Utrecht, GA 3508, the Netherlands.
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Źródło :
Medical image analysis [Med Image Anal] 2022 Jul; Vol. 79, pp. 102470. Date of Electronic Publication: 2022 May 04.
Typ publikacji :
Journal Article; Review; Research Support, Non-U.S. Gov't
MeSH Terms :
Artificial Intelligence*
Deep Learning*
Humans
Czasopismo naukowe
Tytuł :
Explainable deep learning in healthcare: A methodological survey from an attribution view.
Autorzy :
Jin D; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Sergeeva E; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Weng WH; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Chauhan G; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Szolovits P; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
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Źródło :
WIREs mechanisms of disease [WIREs Mech Dis] 2022 May; Vol. 14 (3), pp. e1548. Date of Electronic Publication: 2022 Jan 17.
Typ publikacji :
Journal Article; Review; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
MeSH Terms :
Artificial Intelligence*
Deep Learning*
Delivery of Health Care ; Electronic Health Records ; Surveys and Questionnaires
Czasopismo naukowe
Tytuł :
Deep-learning-based Segmentation of Skeletal Muscle Mass in Routine Abdominal CT Scans.
Autorzy :
Kreher R; Department for Simulation and Graphics, University of Magdeburg, Magdeburg, Germany.; Research Campus STIMULATE, Magdeburg, Germany.
Hinnerichs M; Department of Radiology, University Hospital, Magdeburg, Germany.
Preim B; Department for Simulation and Graphics, University of Magdeburg, Magdeburg, Germany.; Research Campus STIMULATE, Magdeburg, Germany.
Saalfeld S; Department for Simulation and Graphics, University of Magdeburg, Magdeburg, Germany.; Research Campus STIMULATE, Magdeburg, Germany.
Surov A; Department of Radiology, University Hospital, Magdeburg, Germany .
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Źródło :
In vivo (Athens, Greece) [In Vivo] 2022 Jul-Aug; Vol. 36 (4), pp. 1807-1811.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Abdomen ; Humans ; Muscle, Skeletal/diagnostic imaging ; Psoas Muscles/diagnostic imaging ; Retrospective Studies ; Tomography, X-Ray Computed
Czasopismo naukowe
Tytuł :
Learning deep neural networks' architectures using differential evolution. Case study: Medical imaging processing.
Autorzy :
Belciug S; Department of Computer Science, Faculty of Sciences, University of Craiova, Craiova, 200585, Romania. Electronic address: .
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Źródło :
Computers in biology and medicine [Comput Biol Med] 2022 Jul; Vol. 146, pp. 105623. Date of Electronic Publication: 2022 May 17.
Typ publikacji :
Journal Article
MeSH Terms :
COVID-19*
Deep Learning*
Artificial Intelligence ; Diagnostic Imaging ; Humans ; Neural Networks, Computer ; Pandemics
Czasopismo naukowe
Tytuł :
Re-Assessment of Applicability of Greulich and Pyle-Based Bone Age to Korean Children Using Manual and Deep Learning-Based Automated Method.
Autorzy :
Hwang J; Department of Radiology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Korea.
Yoon HM; Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea. .
Hwang JY; Department of Radiology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, College of Medicine, Pusan National University, Yangsan, Korea. .
Kim PH; Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
Bak B; University of Ulsan Foundation for Industry Cooperation, Ulsan, Korea.
Bae BU; VUNO, Inc., Seoul, Korea.
Sung J; VUNO, Inc., Seoul, Korea.
Kim HJ; Department of Clinical Epidemiology and Biostatistics, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
Jung AY; Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
Cho YA; Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
Lee JS; Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
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Źródło :
Yonsei medical journal [Yonsei Med J] 2022 Jul; Vol. 63 (7), pp. 683-691.
Typ publikacji :
Journal Article
MeSH Terms :
Age Determination by Skeleton*/methods
Deep Learning*
Aged ; Asians ; Child ; Humans ; Male ; Osteochondrodysplasias ; Radiography ; Republic of Korea
SCR Disease Name :
Pyle disease
Czasopismo naukowe
Tytuł :
Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0.
Autorzy :
Agarwal M; Department of Computer Science Engineering, Bennett University, India.
Agarwal S; Department of Computer Science Engineering, PSIT, Kanpur, India; Advanced Knowledge Engineering Centre, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA.
Saba L; Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy.
Chabert GL; Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy.
Gupta S; Department of Computer Science Engineering, Bennett University, India.
Carriero A; Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy.
Pasche A; Depart of Radiology, 'Maggiore della Carità' Hospital, University of Piemonte Orientale, Via Solaroli 17, 28100, Novara, Italy.
Danna P; Depart of Radiology, 'Maggiore della Carità' Hospital, University of Piemonte Orientale, Via Solaroli 17, 28100, Novara, Italy.
Mehmedovic A; University Hospital for Infectious Diseases, Zagreb, Croatia.
Faa G; Department of Pathology - AOU of Cagliari, Italy.
Shrivastava S; College of Computing Sciences and IT, Teerthanker Mahaveer University, Moradabad, 244001, India.
Jain K; College of Computing Sciences and IT, Teerthanker Mahaveer University, Moradabad, 244001, India.
Jain H; College of Computing Sciences and IT, Teerthanker Mahaveer University, Moradabad, 244001, India.
Jujaray T; Dept of Molecular, Cell and Developmental Biology, University of California, Santa Cruz, CA, USA.
Singh IM; AtheroPoint LLC, CA, USA.
Turk M; The Hanse-Wissenschaftskolleg Institute for Advanced Study, Delmenhorst, Germany.
Chadha PS; AtheroPoint LLC, CA, USA.
Johri AM; Division of Cardiology, Queen's University, Kingston, Ontario, Canada.
Khanna NN; Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India.
Mavrogeni S; Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece.
Laird JR; Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA.
Sobel DW; Minimally Invasive Urology Institute, Brown University, Providence, RI, USA.
Miner M; Men's Health Center, Miriam Hospital Providence, Rhode Island, USA.
Balestrieri A; Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy.
Sfikakis PP; Rheumatology Unit, National Kapodistrian University of Athens, Greece.
Tsoulfas G; Aristoteleion University of Thessaloniki, Thessaloniki, Greece.
Misra DP; Dept. of Immunology, SGPIMS, Lucknow, UP, India.
Agarwal V; Dept. of Immunology, SGPIMS, Lucknow, UP, India.
Kitas GD; Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK; Arthritis Research UK Epidemiology Unit, Manchester University, Manchester, UK.
Teji JS; Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, USA.
Al-Maini M; Allergy, Clinical Immunology and Rheumatology Institute, Toronto, Canada.
Dhanjil SK; AtheroPoint LLC, CA, USA.
Nicolaides A; Vascular Screening and Diagnostic Centre and Univ. of Nicosia Medical School, Cyprus.
Sharma A; Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA.
Rathore V; AtheroPoint LLC, CA, USA.
Fatemi M; Dept. of Physiology & Biomedical Engg., Mayo Clinic College of Medicine and Science, MN, USA.
Alizad A; Dept. of Radiology, Mayo Clinic College of Medicine and Science, MN, USA.
Krishnan PR; Neurology Department, Fortis Hospital, Bangalore, India.
Yadav RR; Radiodiagnosis, SGPIMS, Lucknow, Uttar Pradesh, India.
Nagy F; Department of Radiology, University of Szeged, 6725, Hungary.
Kincses ZT; Department of Radiology, University of Szeged, 6725, Hungary.
Ruzsa Z; Invasive Cardiology Division, University of Szeged, Budapest, Hungary.
Naidu S; Electrical Engineering Department, University of Minnesota, Duluth, MN, USA.
Viskovic K; University Hospital for Infectious Diseases, Zagreb, Croatia.
Kalra MK; Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
Suri JS; College of Computing Sciences and IT, Teerthanker Mahaveer University, Moradabad, 244001, India; Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, USA. Electronic address: .
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Źródło :
Computers in biology and medicine [Comput Biol Med] 2022 Jul; Vol. 146, pp. 105571. Date of Electronic Publication: 2022 May 21.
Typ publikacji :
Journal Article; Multicenter Study
MeSH Terms :
COVID-19*/diagnostic imaging
Deep Learning*
COVID-19 Testing ; Humans ; Image Processing, Computer-Assisted/methods ; Lung/diagnostic imaging ; Neural Networks, Computer ; Reproducibility of Results ; Tomography, X-Ray Computed/methods
Czasopismo naukowe
Tytuł :
Pediatric age estimation from radiographs of the knee using deep learning.
Autorzy :
Demircioğlu A; Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, D-45147, Essen, Germany. .
Quinsten AS; Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, D-45147, Essen, Germany.
Forsting M; Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, D-45147, Essen, Germany.
Umutlu L; Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, D-45147, Essen, Germany.
Nassenstein K; Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, Hufelandstr. 55, D-45147, Essen, Germany.
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Źródło :
European radiology [Eur Radiol] 2022 Jul; Vol. 32 (7), pp. 4813-4822. Date of Electronic Publication: 2022 Mar 01.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Adolescent ; Child ; Humans ; Knee ; Neural Networks, Computer ; Radiography ; Retrospective Studies
Czasopismo naukowe
Tytuł :
Clinically applicable deep learning-based decision aids for treatment of neovascular AMD.
Autorzy :
Gutfleisch M; Department of Ophthalmology, St. Franziskus-Hospital, Hohenzollernring 74, 48145, Muenster, Germany. .; M3 Macula Monitor Muenster GmbH & Co KG, Muenster, Germany. .
Ester O; Westphalia DataLab GmbH, Muenster, Germany.
Aydin S; Westphalia DataLab GmbH, Muenster, Germany.
Quassowski M; Westphalia DataLab GmbH, Muenster, Germany.
Spital G; Department of Ophthalmology, St. Franziskus-Hospital, Hohenzollernring 74, 48145, Muenster, Germany.; M3 Macula Monitor Muenster GmbH & Co KG, Muenster, Germany.
Lommatzsch A; Department of Ophthalmology, St. Franziskus-Hospital, Hohenzollernring 74, 48145, Muenster, Germany.; M3 Macula Monitor Muenster GmbH & Co KG, Muenster, Germany.; Department of Ophthalmology, University Duisburg-Essen, Essen, Germany.; Achim Wessing Institute of Ophthalmic Diagnostic, University Duisburg-Essen, Essen, Germany.
Rothaus K; Department of Ophthalmology, St. Franziskus-Hospital, Hohenzollernring 74, 48145, Muenster, Germany.; M3 Macula Monitor Muenster GmbH & Co KG, Muenster, Germany.
Dubis AM; NIHR Biomedical Resource Centre, UCL Institute of Ophthalmology and Moorfields Eye Hospital NHS Trust, London, United Kingdom.
Pauleikhoff D; Department of Ophthalmology, St. Franziskus-Hospital, Hohenzollernring 74, 48145, Muenster, Germany.; M3 Macula Monitor Muenster GmbH & Co KG, Muenster, Germany.; Department of Ophthalmology, University Duisburg-Essen, Essen, Germany.; Achim Wessing Institute of Ophthalmic Diagnostic, University Duisburg-Essen, Essen, Germany.
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Źródło :
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie [Graefes Arch Clin Exp Ophthalmol] 2022 Jul; Vol. 260 (7), pp. 2217-2230. Date of Electronic Publication: 2022 Jan 22.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Wet Macular Degeneration*/diagnosis
Wet Macular Degeneration*/drug therapy
Angiogenesis Inhibitors/therapeutic use ; Artificial Intelligence ; Decision Support Techniques ; Humans ; Tomography, Optical Coherence/methods ; Vascular Endothelial Growth Factor A ; Visual Acuity
Czasopismo naukowe
Tytuł :
Comparison of a Deep Learning-Based Reconstruction Algorithm with Filtered Back Projection and Iterative Reconstruction Algorithms for Pediatric Abdominopelvic CT.
Autorzy :
Son W; Department of Radiology, Pusan National University Yangsan Hospital, Yangsan, Korea.
Kim M; School of Biomedical Convergence Engineering, Pusan National University, Busan, Korea.
Hwang JY; Department of Radiology, Pusan National University Yangsan Hospital, Yangsan, Korea.; Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, College of Medicine, Pusan National University, Yangsan, Korea. .
Kim YW; Department of Radiology, Pusan National University Yangsan Hospital, Yangsan, Korea.
Park C; Department of Radiology, Pusan National University Yangsan Hospital, Yangsan, Korea.
Choo KS; Department of Radiology, Pusan National University Yangsan Hospital, Yangsan, Korea.
Kim TU; Department of Radiology, Pusan National University Yangsan Hospital, Yangsan, Korea.
Jang JY; Department of Radiology, Pusan National University Yangsan Hospital, Yangsan, Korea.
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Źródło :
Korean journal of radiology [Korean J Radiol] 2022 Jul; Vol. 23 (7), pp. 752-762. Date of Electronic Publication: 2022 May 27.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Radiographic Image Interpretation, Computer-Assisted*/methods
Adolescent ; Algorithms ; Child ; Child, Preschool ; Humans ; Male ; Radiation Dosage ; Retrospective Studies ; Tomography, X-Ray Computed/methods
Czasopismo naukowe
Tytuł :
Deep learning-based in vivo dose verification from proton-induced secondary-electron-bremsstrahlung images with various count level.
Autorzy :
Yabe T; Department of Integrated Health Science, Nagoya University Graduate School of Medicine, Aichi, Japan; Department of Medical Technology, Nagoya University Hospital, Aichi, Japan. Electronic address: .
Yamaguchi M; Takasaki Advanced Radiation Research Institute, Quantum Beam Science Research Directorate, National Institutes for Quantum Science and Technology (QST), Gunma, Japan.
Liu CC; Department of Biomedical Engineering, University of California, Davis, USA.
Toshito T; Department of Proton Therapy Physics, Nagoya Proton Therapy Center, Nagoya City University West Medical Center, Aichi, Japan.
Kawachi N; Takasaki Advanced Radiation Research Institute, Quantum Beam Science Research Directorate, National Institutes for Quantum Science and Technology (QST), Gunma, Japan.
Yamamoto S; Department of Integrated Health Science, Nagoya University Graduate School of Medicine, Aichi, Japan; Faculty of Science and Engineering, Waseda University, Tokyo, Japan.
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Źródło :
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) [Phys Med] 2022 Jul; Vol. 99, pp. 130-139. Date of Electronic Publication: 2022 Jun 09.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Proton Therapy*
Electrons ; Monte Carlo Method ; Protons
Czasopismo naukowe
Tytuł :
Intracerebral hemorrhage detection on computed tomography images using a residual neural network.
Autorzy :
Altuve M; Valencian International University, Valencia, Spain, and Applied Biophysics and Bioengineering Group, Simon Bolivar University, Caracas, Venezuela. Electronic address: .
Pérez A; Valencian International University, Valencia, Spain. Electronic address: .
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Źródło :
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) [Phys Med] 2022 Jul; Vol. 99, pp. 113-119. Date of Electronic Publication: 2022 Jun 04.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Cerebral Hemorrhage/diagnostic imaging ; Disease Progression ; Humans ; Machine Learning ; Neural Networks, Computer ; Tomography, X-Ray Computed/methods
Czasopismo naukowe
Tytuł :
Automatic head computed tomography image noise quantification with deep learning.
Autorzy :
Inkinen SI; HUS Diagnostic Center, Radiology, Helsinki University and Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland. Electronic address: .
Mäkelä T; HUS Diagnostic Center, Radiology, Helsinki University and Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland; Department of Physics, University of Helsinki, P.O. Box 64, FI-00014 Helsinki, Finland.
Kaasalainen T; HUS Diagnostic Center, Radiology, Helsinki University and Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland.
Peltonen J; HUS Diagnostic Center, Radiology, Helsinki University and Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland.
Kangasniemi M; HUS Diagnostic Center, Radiology, Helsinki University and Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland.
Kortesniemi M; HUS Diagnostic Center, Radiology, Helsinki University and Helsinki University Hospital, Haartmaninkatu 4, 00290 Helsinki, Finland.
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Źródło :
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) [Phys Med] 2022 Jul; Vol. 99, pp. 102-112. Date of Electronic Publication: 2022 Jun 04.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Head/diagnostic imaging ; Humans ; Image Processing, Computer-Assisted/methods ; Neural Networks, Computer ; Tomography, X-Ray Computed/methods
Czasopismo naukowe
Tytuł :
A deep learning radiomics model may help to improve the prediction performance of preoperative grading in meningioma.
Autorzy :
Yang L; PET-CT/MR Department, Harbin Medical University Cancer Hospital, Harbin, China.
Xu P; PET-CT/MR Department, Harbin Medical University Cancer Hospital, Harbin, China.
Zhang Y; PET-CT/MR Department, Harbin Medical University Cancer Hospital, Harbin, China.
Cui N; PET-CT/MR Department, Harbin Medical University Cancer Hospital, Harbin, China.
Wang M; PET-CT/MR Department, Harbin Medical University Cancer Hospital, Harbin, China.
Peng M; PET-CT/MR Department, Harbin Medical University Cancer Hospital, Harbin, China.
Gao C; Medical Imaging Department, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China.
Wang T; Medical Imaging Department, The Second Affiliated Hospital of Harbin Medical University, Harbin, China. .
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Źródło :
Neuroradiology [Neuroradiology] 2022 Jul; Vol. 64 (7), pp. 1373-1382. Date of Electronic Publication: 2022 Jan 17.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Meningeal Neoplasms*/diagnostic imaging
Meningeal Neoplasms*/pathology
Meningeal Neoplasms*/surgery
Meningioma*/diagnostic imaging
Meningioma*/pathology
Meningioma*/surgery
Algorithms ; Cohort Studies ; Humans ; Magnetic Resonance Imaging/methods ; ROC Curve ; Retrospective Studies
Czasopismo naukowe
Tytuł :
Validation of deep learning-based nonspecific estimates for amyloid burden quantification with longitudinal data.
Autorzy :
Nai YH; Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore. Electronic address: .
Liu H; Carnegie Mellon University, Pennsylvania, United States.
Reilhac A; Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
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Corporate Authors :
Alzheimer's Disease Neuroimaging Initiative
Źródło :
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) [Phys Med] 2022 Jul; Vol. 99, pp. 85-93. Date of Electronic Publication: 2022 Jun 02.
Typ publikacji :
Journal Article; Multicenter Study
MeSH Terms :
Alzheimer Disease*/diagnostic imaging
Cognitive Dysfunction*
Deep Learning*
Amyloid beta-Peptides/metabolism ; Brain/metabolism ; Humans ; Positron-Emission Tomography/methods
Czasopismo naukowe
Tytuł :
Treatment plan prediction for lung IMRT using deep learning based fluence map generation.
Autorzy :
Vandewinckele L; Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Belgium; Department of Radiation Oncology, UZ Leuven, Belgium. Electronic address: .
Willems S; Department ESAT/PSI, KU Leuven, Belgium; Medical Imaging Research Center, UZ Leuven, Belgium.
Lambrecht M; Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Belgium; Department of Radiation Oncology, UZ Leuven, Belgium.
Berkovic P; Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Belgium; Department of Radiation Oncology, UZ Leuven, Belgium.
Maes F; Department ESAT/PSI, KU Leuven, Belgium; Medical Imaging Research Center, UZ Leuven, Belgium.
Crijns W; Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Belgium; Department of Radiation Oncology, UZ Leuven, Belgium.
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Źródło :
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) [Phys Med] 2022 Jul; Vol. 99, pp. 44-54. Date of Electronic Publication: 2022 May 21.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Lung Neoplasms*/radiotherapy
Radiotherapy, Intensity-Modulated*
Humans ; Lung ; Radiotherapy Dosage ; Radiotherapy Planning, Computer-Assisted
Czasopismo naukowe
Tytuł :
Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology.
Autorzy :
Ghaffari Laleh N; Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
Muti HS; Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
Loeffler CML; Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
Echle A; Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
Saldanha OL; Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
Mahmood F; Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Lu MY; Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Trautwein C; Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
Langer R; Institute of Pathology and Molecular Pathology, Kepler University Hospital, Johannes Kepler University Linz, Linz, Austria.
Dislich B; Institute of Pathology, University of Bern, Switzerland.
Buelow RD; Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany.
Grabsch HI; Department of Pathology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands.; Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.
Brenner H; Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
Chang-Claude J; Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Cancer Epidemiology Group, University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Alwers E; Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Brinker TJ; Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Khader F; Department of Radiology, University Hospital RWTH Aachen, Aachen, Germany.
Truhn D; Department of Radiology, University Hospital RWTH Aachen, Aachen, Germany.
Gaisa NT; Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany.
Boor P; Institute of Pathology, University Hospital RWTH Aachen, Aachen, Germany.
Hoffmeister M; Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Schulz V; Department of Physics of Molecular Imaging Systems, Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany; Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany; Comprehensive Diagnostic Center Aachen (CDCA), University Hospital Aachen, Aachen, Germany; Hyperion Hybrid Imaging Systems GmbH, Aachen, Germany.
Kather JN; Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany; Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK; Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany. Electronic address: .
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Źródło :
Medical image analysis [Med Image Anal] 2022 Jul; Vol. 79, pp. 102474. Date of Electronic Publication: 2022 May 04.
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms :
Deep Learning*
Artificial Intelligence ; Benchmarking ; Humans ; Neural Networks, Computer ; Supervised Machine Learning
Czasopismo naukowe
Tytuł :
Algal bloom forecasting with time-frequency analysis: A hybrid deep learning approach.
Autorzy :
Liu M; Ocean College, Zhejiang University, #1 Zheda Road, Zhoushan, Zhejiang 316021, China.
He J; Ocean College, Zhejiang University, #1 Zheda Road, Zhoushan, Zhejiang 316021, China; Ocean Academy, Zhejiang University, #1 Zheda Road, Zhoushan, Zhejiang 316021, China.
Huang Y; Ocean College, Zhejiang University, #1 Zheda Road, Zhoushan, Zhejiang 316021, China.
Tang T; Ocean College, Zhejiang University, #1 Zheda Road, Zhoushan, Zhejiang 316021, China.
Hu J; Ocean College, Zhejiang University, #1 Zheda Road, Zhoushan, Zhejiang 316021, China.
Xiao X; Ocean College, Zhejiang University, #1 Zheda Road, Zhoushan, Zhejiang 316021, China; Key Laboratory of Watershed Non-point Source Pollution Control and Water Eco-security of Ministry of Water Resources, College of Environmental and Resources Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China. Electronic address: .
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Źródło :
Water research [Water Res] 2022 Jul 01; Vol. 219, pp. 118591. Date of Electronic Publication: 2022 May 14.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Eutrophication ; Forecasting ; Lakes ; Neural Networks, Computer
Czasopismo naukowe
Tytuł :
Pulmonary emphysema quantification at low dose chest CT using Deep Learning image reconstruction.
Autorzy :
Ferri F; Department of Radiology, Amiens University Hospital, 1 Rond-Point du Professeur Christian Cabrol, F-80054 Amiens Cedex 01, France.
Bouzerar R; Biophysics and Image Processing Unit, Amiens University Hospital, Amiens, France.
Auquier M; Department of Radiology, Amiens University Hospital, 1 Rond-Point du Professeur Christian Cabrol, F-80054 Amiens Cedex 01, France.
Vial J; Department of Radiology, Amiens University Hospital, 1 Rond-Point du Professeur Christian Cabrol, F-80054 Amiens Cedex 01, France.
Renard C; Department of Radiology, Amiens University Hospital, 1 Rond-Point du Professeur Christian Cabrol, F-80054 Amiens Cedex 01, France. Electronic address: .
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Źródło :
European journal of radiology [Eur J Radiol] 2022 Jul; Vol. 152, pp. 110338. Date of Electronic Publication: 2022 May 05.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Emphysema*
Pulmonary Emphysema*/diagnostic imaging
Algorithms ; Humans ; Image Processing, Computer-Assisted ; Radiation Dosage ; Radiographic Image Interpretation, Computer-Assisted/methods ; Retrospective Studies ; Tomography, X-Ray Computed/methods
Czasopismo naukowe
Tytuł :
Diagnostic performance for detecting bone marrow edema of the hip on dual-energy CT: Deep learning model vs. musculoskeletal physicians and radiologists.
Autorzy :
Park C; School of Biomedical Convergence Engineering, College of Information and BioMedical Engineering, Pusan National University, Yangsan, Korea.
Kim M; School of Biomedical Convergence Engineering, College of Information and BioMedical Engineering, Pusan National University, Yangsan, Korea.
Park C; Department of Radiology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Korea. Electronic address: .
Son W; Department of Radiology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Korea.
Lee SM; Department of Orthopedics, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Korea.
Seok Jeong H; Department of Radiology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Korea.
Kang J; School of Biomedical Convergence Engineering, College of Information and BioMedical Engineering, Pusan National University, Yangsan, Korea.
Choi MH; Department of Preventive and Occupational & Environmental Medicine, Pusan National University Yangsan Hospital, Pusan National University, Yangsan, Korea.
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Źródło :
European journal of radiology [Eur J Radiol] 2022 Jul; Vol. 152, pp. 110337. Date of Electronic Publication: 2022 Apr 30.
Typ publikacji :
Journal Article
MeSH Terms :
Bone Marrow Diseases*/diagnostic imaging
Deep Learning*
Adult ; Aged ; Bone Marrow/diagnostic imaging ; Edema/diagnostic imaging ; Female ; Humans ; Middle Aged ; Radiologists ; Retrospective Studies ; Sensitivity and Specificity ; Tomography, X-Ray Computed/methods
Czasopismo naukowe
Tytuł :
Clinical feasibility of an abdominal thin-slice breath-hold single-shot fast spin echo sequence processed using a deep learning-based noise-reduction approach.
Autorzy :
Tajima T; Department of Radiology, International University of Health and Welfare Mita Hospital, 1-4-3 Mita, Minato-ku, Tokyo 108-8329, Japan; Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda Narita, Chiba 286-0124, Japan.
Akai H; Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan.
Yasaka K; Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.
Kunimatsu A; Department of Radiology, International University of Health and Welfare Mita Hospital, 1-4-3 Mita, Minato-ku, Tokyo 108-8329, Japan.
Akahane M; Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda Narita, Chiba 286-0124, Japan.
Yoshioka N; Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda Narita, Chiba 286-0124, Japan.
Abe O; Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.
Ohtomo K; International University of Health and Welfare, 2600-1 kitakanamaru, Otawara, Tochigi 324-8501, Japan.
Kiryu S; Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda Narita, Chiba 286-0124, Japan. Electronic address: .
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Źródło :
Magnetic resonance imaging [Magn Reson Imaging] 2022 Jul; Vol. 90, pp. 76-83. Date of Electronic Publication: 2022 Apr 30.
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms :
Deep Learning*
Breath Holding ; Feasibility Studies ; Humans ; Magnetic Resonance Imaging/methods ; Signal-To-Noise Ratio
Czasopismo naukowe

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