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Wyszukujesz frazę ""Deep Learning"" wg kryterium: Temat


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Tytuł :
Ensemble deep learning model for predicting anterior cruciate ligament tear from lateral knee radiograph.
Autorzy :
Kim DH; Department of Radiology, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea.
Chai JW; Department of Radiology, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea.
Kang JH; Department of Radiology, Konkuk University Medical Center, 120-1 Neungdong-ro, Gwangjin-gu, Seoul, 05030, Republic of Korea. .
Lee JH; Department of Radiology, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea.
Kim HJ; Department of Radiology, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea.
Seo J; Department of Radiology, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea.
Choi JW; Armed Forces Yangju Hospital, Yangju, Republic of Korea.; Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
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Źródło :
Skeletal radiology [Skeletal Radiol] 2022 Dec; Vol. 51 (12), pp. 2269-2279. Date of Electronic Publication: 2022 Jun 09.
Typ publikacji :
Journal Article
MeSH Terms :
Anterior Cruciate Ligament Injuries*/diagnostic imaging
Deep Learning*
Femur ; Humans ; Knee Joint/diagnostic imaging ; Magnetic Resonance Imaging/methods ; Radiography
Czasopismo naukowe
Tytuł :
Multimodal attention-based deep learning for Alzheimer's disease diagnosis.
Autorzy :
Golovanevsky M; Department of Computer Science, Brown University, Providence, Rhode Island, USA.
Eickhoff C; Department of Computer Science, Brown University, Providence, Rhode Island, USA.; Center for Biomedical Informatics, Brown University, Providence, Rhode Island, USA.
Singh R; Department of Computer Science, Brown University, Providence, Rhode Island, USA.; Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, USA.
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Źródło :
Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2022 Nov 14; Vol. 29 (12), pp. 2014-2022.
Typ publikacji :
Journal Article
MeSH Terms :
Alzheimer Disease*/diagnosis
Deep Learning*
Cognitive Dysfunction*/diagnosis
Humans ; Magnetic Resonance Imaging/methods ; Machine Learning
Czasopismo naukowe
Tytuł :
Non-small cell lung cancer diagnosis aid with histopathological images using Explainable Deep Learning techniques.
Autorzy :
Civit-Masot J; Architecture and Computer Technology department (ATC), Robotics and Technology of Computers Lab (RTC), E.T.S. Ingeniería Informática, Avda. Reina Mercedes s/n, Universidad de Sevilla, Seville, 41012, Spain.
Bañuls-Beaterio A; Architecture and Computer Technology department (ATC), Robotics and Technology of Computers Lab (RTC), E.T.S. Ingeniería Informática, Avda. Reina Mercedes s/n, Universidad de Sevilla, Seville, 41012, Spain.
Domínguez-Morales M; Architecture and Computer Technology department (ATC), Robotics and Technology of Computers Lab (RTC), E.T.S. Ingeniería Informática, Avda. Reina Mercedes s/n, Universidad de Sevilla, Seville, 41012, Spain; Computer Engineering Research Institute (I3US), E.T.S. Ingeniería Informática, Avda. Reina Mercedes s/n, Universidad de Sevilla, Seville, 41012, Spain. Electronic address: .
Rivas-Pérez M; Architecture and Computer Technology department (ATC), Robotics and Technology of Computers Lab (RTC), E.T.S. Ingeniería Informática, Avda. Reina Mercedes s/n, Universidad de Sevilla, Seville, 41012, Spain.
Muñoz-Saavedra L; Architecture and Computer Technology department (ATC), Robotics and Technology of Computers Lab (RTC), E.T.S. Ingeniería Informática, Avda. Reina Mercedes s/n, Universidad de Sevilla, Seville, 41012, Spain.
Rodríguez Corral JM; Computer Science department, School of Engineering, Avda. Universidad de Cádiz 10, Universidad de Cádiz, Puerto Real (Cádiz), 11519, Spain.
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Źródło :
Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2022 Nov; Vol. 226, pp. 107108. Date of Electronic Publication: 2022 Sep 07.
Typ publikacji :
Journal Article
MeSH Terms :
Carcinoma, Non-Small-Cell Lung*/diagnostic imaging
Deep Learning*
Lung Neoplasms*/diagnostic imaging
Adenocarcinoma*
Humans ; Artificial Intelligence
Czasopismo naukowe
Tytuł :
Validation of a deep learning-based material estimation model for Monte Carlo dose calculation in proton therapy.
Autorzy :
Chang CW; Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America.
Zhou S; Department of Radiation Oncology, Physics Division, Washington University in St. Louis School of Medicine, St. Louis, MO 63110, United States of America.
Gao Y; Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America.
Lin L; Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America.
Liu T; Department of Radiation Oncology, Mount Sinai Medical Center, New York, NY 10029, United States of America.
Bradley JD; Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America.
Zhang T; Department of Radiation Oncology, Physics Division, Washington University in St. Louis School of Medicine, St. Louis, MO 63110, United States of America.
Zhou J; Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America.
Yang X; Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America.; Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA 30308, United States of America.
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Źródło :
Physics in medicine and biology [Phys Med Biol] 2022 Oct 19; Vol. 67 (21). Date of Electronic Publication: 2022 Oct 19.
Typ publikacji :
Journal Article; Research Support, N.I.H., Extramural
MeSH Terms :
Proton Therapy*/methods
Deep Learning*
Swine ; Animals ; Protons ; Monte Carlo Method ; Phantoms, Imaging ; Water ; Radiotherapy Planning, Computer-Assisted/methods
Czasopismo naukowe
Tytuł :
Symbolic Deep Networks: A Psychologically Inspired Lightweight and Efficient Approach to Deep Learning.
Autorzy :
Veksler VD; DCS Corp, Alexandria, VA.; Human Systems Integration Division (HSID), U.S. Army DEVCOM Data & Analysis Center (DAC).
Hoffman BE; Human Systems Integration Division (HSID), U.S. Army DEVCOM Data & Analysis Center (DAC).
Buchler N; Human Systems Integration Division (HSID), U.S. Army DEVCOM Data & Analysis Center (DAC).
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Źródło :
Topics in cognitive science [Top Cogn Sci] 2022 Oct; Vol. 14 (4), pp. 702-717. Date of Electronic Publication: 2021 Oct 05.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Humans ; Artificial Intelligence ; Neural Networks, Computer ; Machine Learning ; Cognitive Science
Czasopismo naukowe
Tytuł :
Online music-assisted rehabilitation system for depressed people based on deep learning.
Autorzy :
Heping Y; Conservatory of music, Zhejiang Normal University, Jinhua, Zhejiang 321000, China. Electronic address: .
Bin W; Music and Dance College of Hunan First Normal University, Changsha, Hunan 410000, China.
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Źródło :
Progress in neuro-psychopharmacology & biological psychiatry [Prog Neuropsychopharmacol Biol Psychiatry] 2022 Dec 20; Vol. 119, pp. 110607. Date of Electronic Publication: 2022 Jul 18.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Music*/psychology
Affect ; Humans
Czasopismo naukowe
Tytuł :
Deep learning in acute vertigo diagnosis.
Autorzy :
Rastall DP; The Johns Hopkins University School of Medicine, Department of Neurology, Division of Neuro-Visual & Vestibular Disorders, USA. Electronic address: .
Green K; The Johns Hopkins University School of Medicine, Department of Neurology, Division of Advanced Clinical Neurology, USA.
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Źródło :
Journal of the neurological sciences [J Neurol Sci] 2022 Dec 15; Vol. 443, pp. 120454. Date of Electronic Publication: 2022 Oct 19.
Typ publikacji :
Review; Journal Article
MeSH Terms :
Dizziness*/complications
Deep Learning*
Humans ; Artificial Intelligence ; Vertigo/diagnosis ; Vertigo/etiology ; Eye Movements
Czasopismo naukowe
Tytuł :
Discovery of novel SARS-CoV-2 3CL protease covalent inhibitors using deep learning-based screen.
Autorzy :
Wang L; BNLMS, Peking-Tsinghua Center for Life Sciences at the College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, PR China.
Yu Z; BNLMS, Peking-Tsinghua Center for Life Sciences at the College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, PR China.
Wang S; BNLMS, Peking-Tsinghua Center for Life Sciences at the College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, PR China.
Guo Z; BNLMS, Peking-Tsinghua Center for Life Sciences at the College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, PR China.
Sun Q; BNLMS, Peking-Tsinghua Center for Life Sciences at the College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, PR China; Research Unit of Drug Design Method, Chinese Academy of Medical Sciences (2021RU014), Beijing, 100871, PR China. Electronic address: .
Lai L; BNLMS, Peking-Tsinghua Center for Life Sciences at the College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, PR China; Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, PR China; Research Unit of Drug Design Method, Chinese Academy of Medical Sciences (2021RU014), Beijing, 100871, PR China. Electronic address: .
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Źródło :
European journal of medicinal chemistry [Eur J Med Chem] 2022 Dec 15; Vol. 244, pp. 114803. Date of Electronic Publication: 2022 Oct 03.
Typ publikacji :
Journal Article
MeSH Terms :
COVID-19*/drug therapy
Deep Learning*
Humans ; SARS-CoV-2 ; Protease Inhibitors/pharmacology ; Protease Inhibitors/chemistry ; Coronavirus 3C Proteases ; Cysteine ; Antiviral Agents/pharmacology
Czasopismo naukowe
Tytuł :
Development of non-bias phenotypic drug screening for cardiomyocyte hypertrophy by image segmentation using deep learning.
Autorzy :
Komuro J; Department of Cardiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan. Electronic address: sieg_.
Tokuoka Y; Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama-shi, Kanagawa, 223-8522, Japan. Electronic address: .
Seki T; Department of Healthcare Information Management, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan. Electronic address: .
Kusumoto D; Department of Cardiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan. Electronic address: .
Hashimoto H; Department of Cardiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan. Electronic address: .
Katsuki T; Department of Cardiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan; Department of Cardiology, Saitama Municipal Hospital, 2460 Mimuro, Midori-ku, Saitama-shi, Saitama, Japan. Electronic address: .
Nakamura T; Department of Cardiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan. Electronic address: .
Akiba Y; Department of Cardiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan. Electronic address: .
Kuoka T; Department of Cardiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan. Electronic address: .
Kimura M; Department of Cardiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan. Electronic address: .
Yamada T; Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama-shi, Kanagawa, 223-8522, Japan. Electronic address: .
Fukuda K; Department of Cardiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan. Electronic address: .
Funahashi A; Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama-shi, Kanagawa, 223-8522, Japan. Electronic address: .
Yuasa S; Department of Cardiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan. Electronic address: .
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Źródło :
Biochemical and biophysical research communications [Biochem Biophys Res Commun] 2022 Dec 03; Vol. 632, pp. 181-188. Date of Electronic Publication: 2022 Oct 01.
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms :
Cardiomegaly*/diagnostic imaging
Cardiomegaly*/drug therapy
Deep Learning*
Drug Evaluation, Preclinical*/methods
Heart Failure*/drug therapy
Animals ; Mice ; Rats ; Angiotensin II/pharmacology ; Cells, Cultured ; Cholesterol ; Endothelin-1 ; Ezetimibe ; Myocytes, Cardiac/cytology ; Myocytes, Cardiac/drug effects
Czasopismo naukowe
Tytuł :
A Matched-Pair Analysis of Nuclear Morphologic Features Between Core Needle Biopsy and Surgical Specimen in Thyroid Tumors Using a Deep Learning Model.
Autorzy :
Haq F; Department of Biomedicine and Health Sciences, The Catholic University of Korea, Seoul, 06591, Republic of Korea.; College of Medicine, Cancer Research Institute, The Catholic University of Korea, Seoul, 06591, Republic of Korea.
Bychkov A; Department of Pathology, Kameda Medical Center, Kamogawa, Chiba, 296-8602, Japan.
Jung CK; Department of Biomedicine and Health Sciences, The Catholic University of Korea, Seoul, 06591, Republic of Korea. .; College of Medicine, Cancer Research Institute, The Catholic University of Korea, Seoul, 06591, Republic of Korea. .; Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul, 06591, Republic of Korea. .; Department of Pathology, Seoul St. Mary's Hospital, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea. .
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Źródło :
Endocrine pathology [Endocr Pathol] 2022 Dec; Vol. 33 (4), pp. 472-483. Date of Electronic Publication: 2022 Oct 14.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Thyroid Neoplasms*/diagnosis
Thyroid Neoplasms*/pathology
Thyroid Nodule*/diagnosis
Humans ; Biopsy, Large-Core Needle ; Matched-Pair Analysis ; Retrospective Studies
Czasopismo naukowe
Tytuł :
Prediction of Decompensation and Death in Advanced Chronic Liver Disease Using Deep Learning Analysis of Gadoxetic Acid-Enhanced MRI.
Autorzy :
Heo S; Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.; Department of Radiology, Ajou University School of Medicine, Suwon, Korea.
Lee SS; Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea. .
Kim SY; Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Lim YS; Department of Gastroenterology, Liver Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Park HJ; Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Yoon JS; Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea.
Suk HI; Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea.; Department of Artificial Intelligence, Korea University, Seoul, Korea.
Sung YS; Clinical Research Center, Asan Medical Center, Seoul, Korea.
Park B; Health Innovation Big Data Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea.
Lee JS; Department of Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
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Źródło :
Korean journal of radiology [Korean J Radiol] 2022 Dec; Vol. 23 (12), pp. 1269-1280.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Carcinoma, Hepatocellular*
Liver Neoplasms*/diagnostic imaging
Humans ; Male ; Prospective Studies ; Magnetic Resonance Imaging
Czasopismo naukowe
Tytuł :
Automated Measurement of Native T1 and Extracellular Volume Fraction in Cardiac Magnetic Resonance Imaging Using a Commercially Available Deep Learning Algorithm.
Autorzy :
Chang S; Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Han K; Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.
Lee S; Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.
Yang YJ; Phantomics, Inc., Seoul, Korea.
Kim PK; Phantomics, Inc., Seoul, Korea.
Choi BW; Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.; Phantomics, Inc., Seoul, Korea.
Suh YJ; Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea. .
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Źródło :
Korean journal of radiology [Korean J Radiol] 2022 Dec; Vol. 23 (12), pp. 1251-1259.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Humans ; Heart ; Algorithms ; Magnetic Resonance Imaging ; Myocardium
Czasopismo naukowe
Tytuł :
Deep learning-based acceleration of Compressed Sense MR imaging of the ankle.
Autorzy :
Foreman SC; Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany. .
Neumann J; Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany.
Han J; Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany.
Harrasser N; Department of Orthopaedic Surgery, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany.
Weiss K; Philips GmbH, Röntgenstrasse 22, 22335, Hamburg, Germany.
Peeters JM; Philips Healthcare, Veenpluis 4-6, Building QR-0.113, 5684, Best, PC, Netherlands.
Karampinos DC; Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany.
Makowski MR; Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany.
Gersing AS; Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany.; Department of Neuroradiology, University Hospital Munich (LMU), Marchioninistrasse 15, 81377, Munich, Germany.
Woertler K; Department of Radiology, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany.
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Źródło :
European radiology [Eur Radiol] 2022 Dec; Vol. 32 (12), pp. 8376-8385. Date of Electronic Publication: 2022 Jun 25.
Typ publikacji :
Journal Article
MeSH Terms :
Artifacts*
Deep Learning*
Humans ; Signal-To-Noise Ratio ; Ankle/diagnostic imaging ; Prospective Studies ; Artificial Intelligence ; Magnetic Resonance Imaging/methods ; Acceleration ; Imaging, Three-Dimensional/methods
Czasopismo naukowe
Tytuł :
Can deep learning improve image quality of low-dose CT: a prospective study in interstitial lung disease.
Autorzy :
Zhao R; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China.
Sui X; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China.
Qin R; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China.
Du H; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China.
Song L; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China.
Tian D; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China.
Wang J; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China.
Lu X; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China.
Wang Y; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China.
Song W; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China. .
Jin Z; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China. .
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Źródło :
European radiology [Eur Radiol] 2022 Dec; Vol. 32 (12), pp. 8140-8151. Date of Electronic Publication: 2022 Jun 24.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Lung Diseases, Interstitial*/diagnostic imaging
Humans ; Radiographic Image Interpretation, Computer-Assisted/methods ; Radiation Dosage ; Prospective Studies ; Tomography, X-Ray Computed/methods ; Algorithms
Czasopismo naukowe
Tytuł :
Deep Learning-based calculation of patient size and attenuation surrogates from localizer Image: Toward personalized chest CT protocol optimization.
Autorzy :
Salimi Y; Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland.
Shiri I; Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland.
Akhavanallaf A; Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland.
Mansouri Z; Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland.
Sanaat A; Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland.
Pakbin M; Imaging Department, Qom University of Medical Sciences, Qum, Iran.
Ghasemian M; Department of Radiology, Shahid Beheshti Hospital, Qom University of Medical Sciences, Qum, Iran.
Arabi H; Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland.
Zaidi H; Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark. Electronic address: .
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Źródło :
European journal of radiology [Eur J Radiol] 2022 Dec; Vol. 157, pp. 110602. Date of Electronic Publication: 2022 Nov 11.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Humans ; Female ; Male ; Adult ; Middle Aged ; Aged ; Thorax ; Tomography, X-Ray Computed ; Algorithms ; Calibration
Czasopismo naukowe
Tytuł :
Feasibility of deep learning k-space-to-image reconstruction for diffusion weighted imaging in patients with breast cancers: Focus on image quality and reduced scan time.
Autorzy :
Lee EJ; Department of Radiology, Soonchunhyang University Seoul Hospital, 59 Daesakwan-ro, Yongsan-ku, Seoul 04401, Korea.
Chang YW; Department of Radiology, Soonchunhyang University Seoul Hospital, 59 Daesakwan-ro, Yongsan-ku, Seoul 04401, Korea. Electronic address: .
Sung JK; Siemens Healthineers Ltd, Seoul, Korea.
Thomas B; MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.
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Źródło :
European journal of radiology [Eur J Radiol] 2022 Dec; Vol. 157, pp. 110608. Date of Electronic Publication: 2022 Nov 13.
Typ publikacji :
Journal Article
MeSH Terms :
Breast Neoplasms*/diagnostic imaging
Deep Learning*
Humans ; Female ; Feasibility Studies ; Diffusion Magnetic Resonance Imaging ; Image Processing, Computer-Assisted
Czasopismo naukowe
Tytuł :
Classification of Thyroid Nodules by Using Deep Learning Radiomics Based on Ultrasound Dynamic Video.
Autorzy :
Zhang C; Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.; Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
Liu D; Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
Huang L; Department of Oncology, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
Zhao Y; Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
Chen L; Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
Guo Y; Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
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Źródło :
Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine [J Ultrasound Med] 2022 Dec; Vol. 41 (12), pp. 2993-3002. Date of Electronic Publication: 2022 May 23.
Typ publikacji :
Journal Article
MeSH Terms :
Thyroid Nodule*/diagnostic imaging
Thyroid Nodule*/pathology
Deep Learning*
Thyroid Neoplasms*/pathology
Humans ; Ultrasonography/methods ; ROC Curve ; Retrospective Studies
Czasopismo naukowe
Tytuł :
Deep learning for ultra-widefield imaging: a scoping review.
Autorzy :
Bhambra N; Faculty of Medicine, McGill University, Montréal, Québec, Canada.
Antaki F; Department of Ophthalmology, Université de Montréal, Montréal, Québec, Canada.; Centre Universitaire d'Ophtalmologie (CUO), Hôpital Maisonneuve-Rosemont, CIUSSS de L'Est-de-L'Île-de-Montréal, 5415 Assumption Blvd, Montréal, Québec, H1T 2M4, Canada.
Malt FE; Faculty of Medicine, McGill University, Montréal, Québec, Canada.
Xu A; Faculty of Medicine, Université de Montréal, Montréal, Québec, Canada.
Duval R; Department of Ophthalmology, Université de Montréal, Montréal, Québec, Canada. .; Centre Universitaire d'Ophtalmologie (CUO), Hôpital Maisonneuve-Rosemont, CIUSSS de L'Est-de-L'Île-de-Montréal, 5415 Assumption Blvd, Montréal, Québec, H1T 2M4, Canada. .
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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 Dec; Vol. 260 (12), pp. 3737-3778. Date of Electronic Publication: 2022 Jul 20.
Typ publikacji :
Journal Article; Review
MeSH Terms :
Deep Learning*
Diabetic Retinopathy*/diagnosis
Eye Diseases*/diagnostic imaging
Retinal Detachment*
Humans ; Research Design
Czasopismo naukowe
Tytuł :
A systematic review on the use of explainability in deep learning systems for computer aided diagnosis in radiology: Limited use of explainable AI?
Autorzy :
Groen AM; Department of Radiology and Nuclear Medicine, Amsterdam Movement Sciences, Amsterdam UMC Location AMC, Amsterdam, Netherlands. Electronic address: .
Kraan R; Department of Radiology and Nuclear Medicine, Amsterdam Movement Sciences, Amsterdam UMC Location AMC, Amsterdam, Netherlands.
Amirkhan SF; Department of Radiology and Nuclear Medicine, Amsterdam Movement Sciences, Amsterdam UMC Location AMC, Amsterdam, Netherlands.
Daams JG; Medical Library, Amsterdam UMC Location AMC, Amsterdam, Netherlands.
Maas M; Department of Radiology and Nuclear Medicine, Amsterdam Movement Sciences, Amsterdam UMC Location AMC, Amsterdam, Netherlands.
Pokaż więcej
Źródło :
European journal of radiology [Eur J Radiol] 2022 Dec; Vol. 157, pp. 110592. Date of Electronic Publication: 2022 Nov 05.
Typ publikacji :
Systematic Review; Meta-Analysis; Journal Article
MeSH Terms :
Deep Learning*
Radiology*
Humans ; Radiography ; Diagnosis, Computer-Assisted ; Computers
Czasopismo naukowe

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