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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 :
DARQ: Deep learning of quality control for stereotaxic registration of human brain MRI to the T1w MNI-ICBM 152 template.
Autorzy :
Fonov VS; Image Processing Laboratory, Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, Quebec H3A2B4, Canada. Electronic address: .
Dadar M; Image Processing Laboratory, Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, Quebec H3A2B4, Canada; Douglas Mental Health Institute, McGill University, Montreal, Quebec, Canada.
Adni TPRG; For the Alzheimer's Disease Neuroimaging Initiative, Canada.
Collins DL; Image Processing Laboratory, Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, Quebec H3A2B4, Canada.
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Źródło :
NeuroImage [Neuroimage] 2022 Aug 15; Vol. 257, pp. 119266. Date of Electronic Publication: 2022 Apr 29.
Typ publikacji :
Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.; Research Support, N.I.H., Extramural
MeSH Terms :
Deep Learning*
Brain/diagnostic imaging ; Humans ; Image Processing, Computer-Assisted/methods ; Magnetic Resonance Imaging/methods ; Quality Control
Czasopismo naukowe
Tytuł :
An integrated 3D CNN-GRU deep learning method for short-term prediction of PM2.5 concentration in urban environment.
Autorzy :
Faraji M; Department of Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, HezarJerib St., Isfahan 81746-73441, Iran. Electronic address: .
Nadi S; Department of Civil and Environmental Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada. Electronic address: .
Ghaffarpasand O; School of Geography, Earth, and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK. Electronic address: .
Homayoni S; Centre Eau Terre Environnement, Institut National de la Recherche Scientifique, Québec, QC G1K 9A9, Canada. Electronic address: .
Downey K; School of Geography, Earth, and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK.
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Źródło :
The Science of the total environment [Sci Total Environ] 2022 Aug 15; Vol. 834, pp. 155324. Date of Electronic Publication: 2022 Apr 19.
Typ publikacji :
Journal Article
MeSH Terms :
Air Pollution*/analysis
Deep Learning*
Iran ; Neural Networks, Computer ; Particulate Matter/analysis
Czasopismo naukowe
Tytuł :
Deep Learning Electrocardiographic Analysis for Detection of Left-Sided Valvular Heart Disease.
Autorzy :
Elias P; Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA.
Poterucha TJ; Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA.
Rajaram V; Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA.
Moller LM; Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA.
Rodriguez V; Department of Biomedical Informatics, Columbia University, New York, New York, USA.
Bhave S; Department of Biomedical Informatics, Columbia University, New York, New York, USA.
Hahn RT; Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA.
Tison G; Division of Cardiology, University of California-San Francisco, San Francisco, California, USA.
Abreau SA; Division of Cardiology, University of California-San Francisco, San Francisco, California, USA.
Barrios J; Division of Cardiology, University of California-San Francisco, San Francisco, California, USA.
Torres JN; Division of Cardiology, Stanford University, Palo Alto, California, USA.
Hughes JW; Division of Cardiology, Stanford University, Palo Alto, California, USA.
Perez MV; Division of Cardiology, Stanford University, Palo Alto, California, USA.
Finer J; NewYork-Presbyterian Hospital, New York, New York, USA.
Kodali S; Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA.
Khalique O; Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA.
Hamid N; Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA.
Schwartz A; Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA.
Homma S; Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA.
Kumaraiah D; Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA.
Cohen DJ; Cardiovascular Research Foundation, New York, New York, USA; Department of Cardiology, St. Francis Hospital, Roslyn, New York, USA.
Maurer MS; Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA.
Einstein AJ; Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA.
Nazif T; Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA.
Leon MB; Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center and NewYork-Presbyterian Hospital, New York, New York, USA; Cardiovascular Research Foundation, New York, New York, USA.
Perotte AJ; Department of Biomedical Informatics, Columbia University, New York, New York, USA. Electronic address: .
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Źródło :
Journal of the American College of Cardiology [J Am Coll Cardiol] 2022 Aug 09; Vol. 80 (6), pp. 613-626.
Typ publikacji :
Journal Article; Multicenter Study
MeSH Terms :
Aortic Valve Insufficiency*/diagnosis
Aortic Valve Stenosis*/diagnosis
Deep Learning*
Heart Valve Diseases*/diagnosis
Heart Valve Diseases*/epidemiology
Mitral Valve Insufficiency*/diagnosis
Mitral Valve Insufficiency*/epidemiology
Electrocardiography ; Humans
Czasopismo naukowe
Tytuł :
A novel multichannel deep learning model for fast denoising of Monte Carlo dose calculations: preclinical applications.
Autorzy :
van Dijk RHW; Department of Biomedical Engineering, Medical Image Analysis group, Eindhoven University of Technology, Eindhoven, The Netherlands.; Smart Scientific Solutions, Maastricht, The Netherlands.
Staut N; Smart Scientific Solutions, Maastricht, The Netherlands.
Wolfs CJA; Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands.
Verhaegen F; Smart Scientific Solutions, Maastricht, The Netherlands.; Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands.
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Źródło :
Physics in medicine and biology [Phys Med Biol] 2022 Aug 08; Vol. 67 (16). Date of Electronic Publication: 2022 Aug 08.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Monte Carlo Method ; Radiotherapy Dosage ; Radiotherapy Planning, Computer-Assisted/methods ; Uncertainty
Czasopismo naukowe
Tytuł :
A deep learning framework for identifying essential proteins based on multiple biological information.
Autorzy :
Yue Y; Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, China. .; School of Information and Computer, Anhui Agricultural University, Hefei, 230036, China. .; School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China. .; State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, 230036, China. .
Ye C; Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, China.; School of Information and Computer, Anhui Agricultural University, Hefei, 230036, China.
Peng PY; Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, China.; School of Information and Computer, Anhui Agricultural University, Hefei, 230036, China.
Zhai HX; Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, China.; School of Information and Computer, Anhui Agricultural University, Hefei, 230036, China.
Ahmad I; Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, China.; School of Information and Computer, Anhui Agricultural University, Hefei, 230036, China.
Xia C; Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, China.; School of Information and Computer, Anhui Agricultural University, Hefei, 230036, China.
Wu YZ; Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, China.; School of Information and Computer, Anhui Agricultural University, Hefei, 230036, China.; State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, 230036, China.
Zhang YH; Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, 230036, China. .; School of Information and Computer, Anhui Agricultural University, Hefei, 230036, China. .; School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China. .
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Źródło :
BMC bioinformatics [BMC Bioinformatics] 2022 Aug 04; Vol. 23 (1), pp. 318. Date of Electronic Publication: 2022 Aug 04.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Computational Biology/methods ; Machine Learning ; Protein Interaction Maps ; Proteins/metabolism ; Saccharomyces cerevisiae/genetics ; Saccharomyces cerevisiae/metabolism
Czasopismo naukowe
Tytuł :
Neurochemical Concentration Prediction Using Deep Learning vs Principal Component Regression in Fast Scan Cyclic Voltammetry: A Comparison Study.
Autorzy :
Choi H; Department of Neurology, Weill Institute for Neuroscience, University of California San Francisco, San Francisco, California 94158, United States.
Shin H; Department of Neurologic Surgery, Mayo Clinic, Rochester, Minnesota 55905, United States.; Department of Biomedical Engineering, Mayo Clinic, Rochester, Minnesota 55905, United States.
Cho HU; Department of Biomedical Engineering, Hanyang University, Seoul 04763, Republic of Korea.
Blaha CD; Department of Neurologic Surgery, Mayo Clinic, Rochester, Minnesota 55905, United States.
Heien ML; Department of Chemistry and Biochemistry, University of Arizona, Tucson, Arizona 85721, United States.
Oh Y; Department of Neurologic Surgery, Mayo Clinic, Rochester, Minnesota 55905, United States.; Department of Biomedical Engineering, Mayo Clinic, Rochester, Minnesota 55905, United States.
Lee KH; Department of Neurologic Surgery, Mayo Clinic, Rochester, Minnesota 55905, United States.; Department of Biomedical Engineering, Mayo Clinic, Rochester, Minnesota 55905, United States.
Jang DP; Department of Biomedical Engineering, Hanyang University, Seoul 04763, Republic of Korea.
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Źródło :
ACS chemical neuroscience [ACS Chem Neurosci] 2022 Aug 03; Vol. 13 (15), pp. 2288-2297. Date of Electronic Publication: 2022 Jul 25.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Brain Stimulation*/methods
Deep Learning*
Dopamine/metabolism ; Electrochemical Techniques/methods ; Neurotransmitter Agents/analysis ; Serotonin/metabolism
Czasopismo naukowe
Tytuł :
Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy.
Autorzy :
Hasan MM; Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA. Electronic address: .
Tsukiyama S; Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.
Cho JY; Molecular Immunology Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Korea.
Kurata H; Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.
Alam MA; Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA.
Liu X; Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA.
Manavalan B; Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Korea. Electronic address: .
Deng HW; Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA. Electronic address: .
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Źródło :
Molecular therapy : the journal of the American Society of Gene Therapy [Mol Ther] 2022 Aug 03; Vol. 30 (8), pp. 2856-2867. Date of Electronic Publication: 2022 May 06.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
RNA*/genetics
Algorithms ; Computational Biology/methods ; Humans ; Machine Learning
Czasopismo naukowe
Tytuł :
Fear memory-associated synaptic and mitochondrial changes revealed by deep learning-based processing of electron microscopy data.
Autorzy :
Liu J; National Laboratory of Pattern Recognition, Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, School of Future Technology, University of the Chinese Academy of Sciences, Beijing 101408, China.
Qi J; School of Life Science and Technology, ShanghaiTech University, Shanghai, China; Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; University of the Chinese Academy of Sciences, Beijing 100049, China.
Chen X; National Laboratory of Pattern Recognition, Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Li Z; National Laboratory of Pattern Recognition, Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, School of Future Technology, University of the Chinese Academy of Sciences, Beijing 101408, China.
Hong B; National Laboratory of Pattern Recognition, Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, School of Future Technology, University of the Chinese Academy of Sciences, Beijing 101408, China.
Ma H; National Laboratory of Pattern Recognition, Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Li G; National Laboratory of Pattern Recognition, Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Shen L; National Laboratory of Pattern Recognition, Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Liu D; Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China.
Kong Y; Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China.
Zhai H; National Laboratory of Pattern Recognition, Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, School of Future Technology, University of the Chinese Academy of Sciences, Beijing 101408, China.
Xie Q; Research Base of Beijing Modern Manufacturing Development, Beijing University of Technology, Beijing 100124, China. Electronic address: .
Han H; National Laboratory of Pattern Recognition, Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, School of Future Technology, University of the Chinese Academy of Sciences, Beijing 101408, China; Institute of Neuroscience, State Key Laboratory of Neuroscience, Key Laboratory of Primate Neurobiology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China. Electronic address: .
Yang Y; School of Life Science and Technology, ShanghaiTech University, Shanghai, China. Electronic address: .
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Źródło :
Cell reports [Cell Rep] 2022 Aug 02; Vol. 40 (5), pp. 111151.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Animals ; Fear ; Mice ; Microscopy, Electron ; Mitochondria/ultrastructure ; Neuronal Plasticity ; Synapses/metabolism
Czasopismo naukowe
Tytuł :
Deep learning in orthopaedic research : weighing idealism against realism.
Autorzy :
Vigdorchik JM; Department of Orthopaedic Surgery, Adult Reconstruction and Joint Replacement Service, New York, New York, USA.
Jang SJ; Department of Orthopaedic Surgery, Adult Reconstruction and Joint Replacement Service, New York, New York, USA.; Weill Cornell Medical College, New York, New York, USA.
Taunton MJ; Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota, USA.
Haddad FS; University College London Hospitals NHS Foundation Trust, The Princess Grace Hospital, and The NIHR Biomedical Research Centre at UCLH, London, UK.; The Bone & Joint Journal, London, UK.
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Źródło :
The bone & joint journal [Bone Joint J] 2022 Aug; Vol. 104-B (8), pp. 909-910.
Typ publikacji :
Editorial
MeSH Terms :
Arthroplasty, Replacement, Knee*
Deep Learning*
Orthopedics*
Clinical Competence ; Humans
Opinia redakcyjna
Tytuł :
Spinopelvic measurements of sagittal balance with deep learning: systematic review and critical evaluation.
Autorzy :
Vrtovec T; Laboratory of Imaging Technologies, Faculty of Electrical Engineering, University of Ljubljana, Tržaška cesta 25, 1000, Ljubljana, Slovenia. .
Ibragimov B; Laboratory of Imaging Technologies, Faculty of Electrical Engineering, University of Ljubljana, Tržaška cesta 25, 1000, Ljubljana, Slovenia.; Department of Computer Science, University of Copenhagen, Universitetsparken 1, 2100, Copenhagen, Denmark.
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Źródło :
European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society [Eur Spine J] 2022 Aug; Vol. 31 (8), pp. 2031-2045. Date of Electronic Publication: 2022 Mar 12.
Typ publikacji :
Journal Article; Systematic Review
MeSH Terms :
Deep Learning*
Cross-Sectional Studies ; Humans ; Lumbar Vertebrae/diagnostic imaging ; Lumbosacral Region/diagnostic imaging ; Pelvis/diagnostic imaging ; Radiography
Czasopismo naukowe
Tytuł :
An externally validated deep learning model for the accurate segmentation of the lumbar paravertebral muscles.
Autorzy :
Niemeyer F; Center for Trauma Research Ulm, Institute of Orthopaedic Research and Biomechanics, Ulm University, Ulm, Germany.
Zanker A; Center for Trauma Research Ulm, Institute of Orthopaedic Research and Biomechanics, Ulm University, Ulm, Germany.
Jonas R; Center for Trauma Research Ulm, Institute of Orthopaedic Research and Biomechanics, Ulm University, Ulm, Germany.
Tao Y; Center for Trauma Research Ulm, Institute of Orthopaedic Research and Biomechanics, Ulm University, Ulm, Germany.
Galbusera F; Spine Center, Schulthess Clinic, Lengghalde 2, 8008, Zurich, Switzerland. .
Wilke HJ; Center for Trauma Research Ulm, Institute of Orthopaedic Research and Biomechanics, Ulm University, Ulm, Germany.
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Źródło :
European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society [Eur Spine J] 2022 Aug; Vol. 31 (8), pp. 2156-2164. Date of Electronic Publication: 2022 Jul 19.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Sarcopenia*/pathology
Humans ; Lumbosacral Region/diagnostic imaging ; Lumbosacral Region/pathology ; Magnetic Resonance Imaging/methods ; Muscles ; Paraspinal Muscles/diagnostic imaging ; Retrospective Studies
Czasopismo naukowe
Tytuł :
Improving interobserver agreement and performance of deep learning models for segmenting acute ischemic stroke by combining DWI with optimized ADC thresholds.
Autorzy :
Juan CJ; Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, Republic of China.; Department of Medical Imaging, China Medical University Hsinchu Hospital, Hsinchu, Taiwan, Republic of China.; Department of Radiology, School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan, Republic of China.; Department of Medical Imaging, China Medical University Hospital, Taichung, Taiwan, Republic of China.; Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan, Republic of China.
Lin SC; Department of Medical Imaging, China Medical University Hsinchu Hospital, Hsinchu, Taiwan, Republic of China.; Ph.D. Program in Electrical and Communication Engineering, Feng Chia University, Taichung, Taiwan, Republic of China.
Li YH; Department of Medical Imaging, China Medical University Hsinchu Hospital, Hsinchu, Taiwan, Republic of China.; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, Republic of China.
Chang CC; Department of Medical Imaging, China Medical University Hsinchu Hospital, Hsinchu, Taiwan, Republic of China.; Department of Management Science, National Chiao-Tung University, Hsinchu, Taiwan, Republic of China.
Jeng YH; Department of Medical Imaging, China Medical University Hsinchu Hospital, Hsinchu, Taiwan, Republic of China.; Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan, Republic of China.
Peng HH; Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan, Republic of China.
Huang TY; Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, Republic of China.
Chung HW; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, Republic of China.; Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan, Republic of China.
Shen WC; Department of Medical Imaging, China Medical University Hsinchu Hospital, Hsinchu, Taiwan, Republic of China.; Department of Radiology, School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan, Republic of China.
Tsai CH; Department of Neurology, China Medical University Hospital, Taichung, Taiwan, Republic of China.
Chang RF; Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, Republic of China. .; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, Republic of China. .
Liu YJ; Department of Automatic Control Engineering, Feng Chia University, No. 100 Wenhwa Rd., Seatwen, 40724, Taichung, Taiwan, Republic of China. .
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Źródło :
European radiology [Eur Radiol] 2022 Aug; Vol. 32 (8), pp. 5371-5381. Date of Electronic Publication: 2022 Feb 24.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Ischemic Stroke*/diagnostic imaging
Stroke*/diagnostic imaging
Diffusion Magnetic Resonance Imaging ; Humans ; Observer Variation
Czasopismo naukowe
Tytuł :
Deep learning approach of diffusion-weighted imaging as an outcome predictor in laryngeal and hypopharyngeal cancer patients with radiotherapy-related curative treatment: a preliminary study.
Autorzy :
Tomita H; Department of Radiology, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511, Japan. .
Kobayashi T; Department of Advanced Biomedical Imaging Informatics, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511, Japan.
Takaya E; School of Science for Open and Environmental Systems, Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa, 223-8522, Japan.
Mishiro S; Department of AI Research Lab, Harada Academy, 2-54-4, Higashitaniyama, Kagoshima, Kagoshima, 891-0113, Japan.
Hirahara D; Department of AI Research Lab, Harada Academy, 2-54-4, Higashitaniyama, Kagoshima, Kagoshima, 891-0113, Japan.
Fujikawa A; Department of Radiology, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511, Japan.
Kurihara Y; Department of Radiology, Machida Municipal Hospital, 2-15-41 Asahi-cho, Machida, Tokyo, 194-0023, Japan.
Mimura H; Department of Radiology, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511, Japan.
Kobayashi Y; Department of Advanced Biomedical Imaging Informatics, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511, Japan.
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Źródło :
European radiology [Eur Radiol] 2022 Aug; Vol. 32 (8), pp. 5353-5361. Date of Electronic Publication: 2022 Feb 24.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Hypopharyngeal Neoplasms*/diagnostic imaging
Hypopharyngeal Neoplasms*/radiotherapy
Chemoradiotherapy/methods ; Diffusion Magnetic Resonance Imaging/methods ; Humans ; Neoplasm Recurrence, Local/therapy ; Prognosis ; Retrospective Studies
Czasopismo naukowe
Tytuł :
Deep learning for Alzheimer's disease diagnosis: A survey.
Autorzy :
Khojaste-Sarakhsi M; Institute for Computing and Information Sciences, Radboud University, Nijmegen, The Netherlands; Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran.
Haghighi SS; Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran. Electronic address: .
Ghomi SMTF; Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran.
Marchiori E; Institute for Computing and Information Sciences, Radboud University, Nijmegen, The Netherlands.
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Źródło :
Artificial intelligence in medicine [Artif Intell Med] 2022 Aug; Vol. 130, pp. 102332. Date of Electronic Publication: 2022 Jun 12.
Typ publikacji :
Journal Article; Review
MeSH Terms :
Alzheimer Disease*/diagnostic imaging
Deep Learning*
Neurodegenerative Diseases*
Humans ; Magnetic Resonance Imaging/methods ; Neural Networks, Computer
Czasopismo naukowe
Tytuł :
Deep learning for image-based liver analysis - A comprehensive review focusing on malignant lesions.
Autorzy :
Survarachakan S; Department of Computer Science, Norwegian University of Science and Technology, 7491 Trondheim, Norway. Electronic address: .
Prasad PJR; The Intervention Centre, Oslo University Hospital, 0372 Oslo, Norway; Department of Informatics, University of Oslo, 0315 Oslo, Norway.
Naseem R; Department of Computer Science, Norwegian University of Science and Technology, 2815 Gjøvik, Norway.
Pérez de Frutos J; Department of Health Research, SINTEF A.S., 7030 Trondheim, Norway.
Kumar RP; The Intervention Centre, Oslo University Hospital, 0372 Oslo, Norway.
Langø T; Department of Health Research, SINTEF A.S., 7030 Trondheim, Norway.
Alaya Cheikh F; Department of Computer Science, Norwegian University of Science and Technology, 2815 Gjøvik, Norway.
Elle OJ; The Intervention Centre, Oslo University Hospital, 0372 Oslo, Norway; Department of Informatics, University of Oslo, 0315 Oslo, Norway.
Lindseth F; Department of Computer Science, Norwegian University of Science and Technology, 7491 Trondheim, Norway.
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Źródło :
Artificial intelligence in medicine [Artif Intell Med] 2022 Aug; Vol. 130, pp. 102331. Date of Electronic Publication: 2022 Jun 09.
Typ publikacji :
Journal Article; Review; Research Support, Non-U.S. Gov't
MeSH Terms :
Deep Learning*
Liver Neoplasms*/diagnostic imaging
Humans ; Image Processing, Computer-Assisted/methods ; Neural Networks, Computer
Czasopismo naukowe
Tytuł :
Use of deep learning in the MRI diagnosis of Chiari malformation type I.
Autorzy :
Tanaka KW; Macquarie Medical School, Macquarie University, NSW, 2109, Sydney, Australia.
Russo C; Macquarie Medical School, Macquarie University, NSW, 2109, Sydney, Australia.; Computational NeuroSurgery (CNS) Lab, Macquarie University, Sydney, Australia.
Liu S; Computational NeuroSurgery (CNS) Lab, Macquarie University, Sydney, Australia.; Centre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia.
Stoodley MA; Macquarie Medical School, Macquarie University, NSW, 2109, Sydney, Australia.
Di Ieva A; Macquarie Medical School, Macquarie University, NSW, 2109, Sydney, Australia. .; Computational NeuroSurgery (CNS) Lab, Macquarie University, Sydney, Australia. .
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Źródło :
Neuroradiology [Neuroradiology] 2022 Aug; Vol. 64 (8), pp. 1585-1592. Date of Electronic Publication: 2022 Feb 24.
Typ publikacji :
Journal Article
MeSH Terms :
Arnold-Chiari Malformation*/diagnostic imaging
Deep Learning*
Adult ; Female ; Humans ; Magnetic Resonance Imaging/methods ; Neural Networks, Computer ; Retrospective Studies
Czasopismo naukowe
Tytuł :
Deep-learning 2.5-dimensional single-shot detector improves the performance of automated detection of brain metastases on contrast-enhanced CT.
Autorzy :
Takao H; Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan. .
Amemiya S; Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Kato S; Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Yamashita H; Department of Radiology, Teikyo University Hospital, Mizonokuchi, 5-1-1 Futago, Takatsu-ku, Kawasaki, Kanagawa, 213-8507, Japan.
Sakamoto N; Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Abe O; Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
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Źródło :
Neuroradiology [Neuroradiology] 2022 Aug; Vol. 64 (8), pp. 1511-1518. Date of Electronic Publication: 2022 Jan 22.
Typ publikacji :
Journal Article
MeSH Terms :
Brain Neoplasms*/diagnostic imaging
Brain Neoplasms*/secondary
Deep Learning*
Humans ; Tomography, X-Ray Computed/methods
Czasopismo naukowe
Tytuł :
DR-only Carbon-ion radiotherapy treatment planning via deep learning.
Autorzy :
Zhang X; Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China.
He P; Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Li Y; Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China; Gansu Provincial Hospital, Lanzhou 730000, China.
Liu X; Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Ma Y; Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Shen G; Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Dai Z; Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Zhang H; Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Chen W; Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China; Putian Lanhai Nuclear Medicine Research Center, Putian 351152, China.
Li Q; Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China; Putian Lanhai Nuclear Medicine Research Center, Putian 351152, China. 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 Aug; Vol. 100, pp. 120-128. Date of Electronic Publication: 2022 Jul 04.
Typ publikacji :
Journal Article
MeSH Terms :
Deep Learning*
Heavy Ion Radiotherapy*
Radiotherapy, Intensity-Modulated*/methods
Carbon ; Cone-Beam Computed Tomography/methods ; Humans ; Image Processing, Computer-Assisted/methods ; Radiographic Image Enhancement ; Radiotherapy Dosage ; Radiotherapy Planning, Computer-Assisted/methods
Czasopismo naukowe
Tytuł :
LRFNet: A deep learning model for the assessment of liver reserve function based on Child-Pugh score and CT image.
Autorzy :
Huang Z; School of Medical Information and Engineering, Southwest Medical University, Luzhou, China.
Zhang G; School of Medical Information and Engineering, Southwest Medical University, Luzhou, China.
Liu J; Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Huang M; Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Zhong L; School of Medical Information and Engineering, Southwest Medical University, Luzhou, China. Electronic address: .
Shu J; Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China; Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, China. Electronic address: .
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Źródło :
Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2022 Aug; Vol. 223, pp. 106993. Date of Electronic Publication: 2022 Jun 30.
Typ publikacji :
Journal Article
MeSH Terms :
Carcinoma, Hepatocellular*/diagnostic imaging
Carcinoma, Hepatocellular*/pathology
Deep Learning*
Liver Neoplasms*/diagnostic imaging
Liver Neoplasms*/pathology
Humans ; Retrospective Studies ; Tomography, X-Ray Computed
Czasopismo naukowe

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