Temat: "Reproducibility of Results" - OpacWWW

Skocz do pozycji: 1.

Tytuł:: Robust Template Matching Using Multiple-Layered Absent Color Indexing.
Autorzy:: Wei G; School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China.
Tian Y; Graduate School of Information Science and Technology, Hokkaido University, Sapporo 060-0814, Japan.
Kaneko S; Graduate School of Information Science and Technology, Hokkaido University, Sapporo 060-0814, Japan.
Jiang Z; School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China.; Pokaż więcej
Źródło:: Sensors (Basel, Switzerland) [Sensors (Basel)] 2022 Sep 03; Vol. 22 (17). Date of Electronic Publication: 2022 Sep 03.
Typ publikacji:: Journal Article
MeSH Terms:: Image Interpretation, Computer-Assisted*/methods
Pattern Recognition, Automated*/methods
Algorithms ; Color ; Image Enhancement/methods ; Reproducibility of Results ; Sensitivity and Specificity

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Skocz do pozycji: 2.

Tytuł:: An inline deep learning based free-breathing ECG-free cine for exercise cardiovascular magnetic resonance.
Autorzy:: Morales MA; Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave., Boston, MA, 02215, USA.
Assana S; Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave., Boston, MA, 02215, USA.
Cai X; Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave., Boston, MA, 02215, USA.; Siemens Medical Solutions USA, Inc, Chicago, IL, USA.
Chow K; Siemens Medical Solutions USA, Inc, Chicago, IL, USA.
Haji-Valizadeh H; Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave., Boston, MA, 02215, USA.
Sai E; Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave., Boston, MA, 02215, USA.
Tsao C; Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave., Boston, MA, 02215, USA.
Matos J; Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave., Boston, MA, 02215, USA.
Rodriguez J; Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave., Boston, MA, 02215, USA.
Berg S; Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave., Boston, MA, 02215, USA.
Whitehead N; Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave., Boston, MA, 02215, USA.
Pierce P; Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave., Boston, MA, 02215, USA.
Goddu B; Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave., Boston, MA, 02215, USA.
Manning WJ; Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave., Boston, MA, 02215, USA.; Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA.
Nezafat R; Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave., Boston, MA, 02215, USA. .; Pokaż więcej
Źródło:: Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance [J Cardiovasc Magn Reson] 2022 Aug 11; Vol. 24 (1), pp. 47. Date of Electronic Publication: 2022 Aug 11.
Typ publikacji:: Journal Article; Research Support, N.I.H., Extramural
MeSH Terms:: Coronary Artery Disease*/diagnostic imaging
Image Interpretation, Computer-Assisted*/methods
Magnetic Resonance Imaging, Cine*/methods
Respiratory-Gated Imaging Techniques*/methods
Deep Learning ; Exercise Test ; Feasibility Studies ; Humans ; Reproducibility of Results

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Skocz do pozycji: 3.

Tytuł:: Deep robust residual network for super-resolution of 2D fetal brain MRI.
Autorzy:: Song L; The school of information and communications engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
Wang Q; Xi'an Institute of Optics and Precision Mechanics of Chinese Academy of Sciences, Xi'an, 710049, China.
Liu T; The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China.
Li H; Xi'an Institute of Optics and Precision Mechanics of Chinese Academy of Sciences, Xi'an, 710049, China.
Fan J; The school of information and communications engineering, Xi'an Jiaotong University, Xi'an, 710049, China. .
Yang J; The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China. .
Hu B; Xi'an Institute of Optics and Precision Mechanics of Chinese Academy of Sciences, Xi'an, 710049, China. .; Pokaż więcej
Źródło:: Scientific reports [Sci Rep] 2022 Jan 10; Vol. 12 (1), pp. 406. Date of Electronic Publication: 2022 Jan 10.
Typ publikacji:: Comparative Study; Journal Article; Research Support, Non-U.S. Gov't; Validation Study
MeSH Terms:: Deep Learning*
Image Interpretation, Computer-Assisted*
Magnetic Resonance Imaging*
Prenatal Diagnosis*
Brain/*diagnostic imaging
Fetus/*diagnostic imaging
Databases, Factual ; Female ; Humans ; Predictive Value of Tests ; Pregnancy ; Reproducibility of Results

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Skocz do pozycji: 4.

Tytuł:: A novel deep learning-based 3D cell segmentation framework for future image-based disease detection.
Autorzy:: Wang A; Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China.
Zhang Q; Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China.
Han Y; Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China.
Megason S; Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
Hormoz S; Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
Mosaliganti KR; Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
Lam JCK; Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China. .
Li VOK; Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China. .; Pokaż więcej
Źródło:: Scientific reports [Sci Rep] 2022 Jan 10; Vol. 12 (1), pp. 342. Date of Electronic Publication: 2022 Jan 10.
Typ publikacji:: Comparative Study; Journal Article
MeSH Terms:: Cell Membrane*
Deep Learning*
Image Interpretation, Computer-Assisted*
Imaging, Three-Dimensional*
Microscopy*
Animals ; Arabidopsis/cytology ; Embryo, Nonmammalian/cytology ; Predictive Value of Tests ; Reproducibility of Results ; Zebrafish/embryology

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Skocz do pozycji: 5.

Tytuł:: Panoramic tongue imaging and deep convolutional machine learning model for diabetes diagnosis in humans.
Autorzy:: Balasubramaniyan S; Department of Electronics and Communication Engineering, Jai Shriram Engineering College, Tiruppur, Tamil Nadu, 638 660, India. .
Jeyakumar V; Department of Biomedical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, Tamil Nadu, 603 110, India.
Nachimuthu DS; Department of Electrical Engineering, National Institute of Technology Arunachal Pradesh, Yupia, Papum Pare District, Arunachal Pradesh, 791112, India.; Pokaż więcej
Źródło:: Scientific reports [Sci Rep] 2022 Jan 07; Vol. 12 (1), pp. 186. Date of Electronic Publication: 2022 Jan 07.
Typ publikacji:: Comparative Study; Journal Article
MeSH Terms:: Deep Learning*
Diagnosis, Computer-Assisted*
Image Interpretation, Computer-Assisted*
Medicine, Chinese Traditional*
Photography*
Diabetes Mellitus, Type 2/*pathology
Tongue/*pathology
Decision Support Techniques ; Humans ; Predictive Value of Tests ; Prognosis ; Reproducibility of Results

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Skocz do pozycji: 6.

Tytuł:: Quantifying the cell morphology and predicting biological behavior of signet ring cell carcinoma using deep learning.
Autorzy:: Da Q; Department of Pathology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Deng S; Department of Pathology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Li J; Sensetime Research, No. 1900 Hongmei Road, Xuhui District, Shanghai, China.
Yi H; Department of Pathology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Huang X; Sensetime Research, No. 1900 Hongmei Road, Xuhui District, Shanghai, China.
Yang X; Department of Pathology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Yu T; Department of Pathology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Wang X; Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Liu J; Department of Pathology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Duan Q; Sensetime Research, No. 1900 Hongmei Road, Xuhui District, Shanghai, China.
Metaxas D; Department of Computer Science, Rutgers The State University of New Jersey, Newark, USA. .
Wang C; Department of Pathology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China. .; Pokaż więcej
Źródło:: Scientific reports [Sci Rep] 2022 Jan 07; Vol. 12 (1), pp. 183. Date of Electronic Publication: 2022 Jan 07.
Typ publikacji:: Journal Article
MeSH Terms:: Cell Shape*
Deep Learning*
Diagnosis, Computer-Assisted*
Image Interpretation, Computer-Assisted*
Microscopy*
Carcinoma, Signet Ring Cell/*pathology
Colorectal Neoplasms/*pathology
Stomach Neoplasms/*pathology
Biopsy ; Humans ; Predictive Value of Tests ; Prognosis ; Reproducibility of Results

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Skocz do pozycji: 7.

Tytuł:: Colonoscopic image synthesis with generative adversarial network for enhanced detection of sessile serrated lesions using convolutional neural network.
Autorzy:: Yoon D; Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, 08826, South Korea.
Kong HJ; Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul, 03080, South Korea.; Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, 03080, South Korea.; Medical Big Data Research Center, Seoul National University College of Medicine, Seoul, 03080, South Korea.; Artificial Intelligence Institute, Seoul National University, Seoul, 08826, South Korea.
Kim BS; Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, 08826, South Korea.
Cho WS; Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, 08826, South Korea.
Lee JC; Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, 03080, South Korea.; Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul, 03080, South Korea.; Institute of Bioengineering, Seoul National University, Seoul, 08826, South Korea.
Cho M; Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul, 03080, South Korea.; Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, South Korea.
Lim MH; Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, 03080, South Korea.
Yang SY; Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, 06236, South Korea.
Lim SH; Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, 06236, South Korea.
Lee J; Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, 06236, South Korea.
Song JH; Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, 06236, South Korea.
Chung GE; Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, 06236, South Korea.
Choi JM; Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, 06236, South Korea.
Kang HY; Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, 06236, South Korea.
Bae JH; Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, 06236, South Korea. .
Kim S; Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, 03080, South Korea. .; Artificial Intelligence Institute, Seoul National University, Seoul, 08826, South Korea. .; Institute of Bioengineering, Seoul National University, Seoul, 08826, South Korea. .; Pokaż więcej
Źródło:: Scientific reports [Sci Rep] 2022 Jan 07; Vol. 12 (1), pp. 261. Date of Electronic Publication: 2022 Jan 07.
Typ publikacji:: Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms:: Colonoscopy*
Early Detection of Cancer*
Image Interpretation, Computer-Assisted*
Neural Networks, Computer*
Colonic Polyps/*pathology
Colorectal Neoplasms/*pathology
Databases, Factual ; Humans ; Predictive Value of Tests ; Prospective Studies ; Reproducibility of Results ; Retrospective Studies

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Skocz do pozycji: 8.

Tytuł:: Identifying diabetes from conjunctival images using a novel hierarchical multi-task network.
Autorzy:: Li X; Eye Hospital, The First Affiliated Hospital of Harbin Medical University, No.143, Yiman Street, Nangang District, Harbin City, 150001, Heilongjiang Province, China.; Key Laboratory of Basic and Clinical Research of Heilongjiang Province, Harbin, 150001, China.; Eye Department, Shanghai Children 's Hospital, Shanghai Jiaotong University, Shanghai, China.
Xia C; State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Room 230, Building 1, Yuquan Campus, 38 Zhe Da Road, Hangzhou, 310027, Zhejiang Province, China.
Li X; School of Electrical Engineering and Computer Science, 2002 Digital Media Center, Louisiana State University, 340 E. Parker Blvd, Baton Rouge, LA, 70803, USA.
Wei S; School of Electrical Engineering and Computer Science, 2002 Digital Media Center, Louisiana State University, 340 E. Parker Blvd, Baton Rouge, LA, 70803, USA.
Zhou S; Eye Hospital, The First Affiliated Hospital of Harbin Medical University, No.143, Yiman Street, Nangang District, Harbin City, 150001, Heilongjiang Province, China.; Key Laboratory of Basic and Clinical Research of Heilongjiang Province, Harbin, 150001, China.
Yu X; Eye Hospital, The First Affiliated Hospital of Harbin Medical University, No.143, Yiman Street, Nangang District, Harbin City, 150001, Heilongjiang Province, China.; Key Laboratory of Basic and Clinical Research of Heilongjiang Province, Harbin, 150001, China.
Gao J; Eye Hospital, The First Affiliated Hospital of Harbin Medical University, No.143, Yiman Street, Nangang District, Harbin City, 150001, Heilongjiang Province, China.; Key Laboratory of Basic and Clinical Research of Heilongjiang Province, Harbin, 150001, China.
Cao Y; State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Room 230, Building 1, Yuquan Campus, 38 Zhe Da Road, Hangzhou, 310027, Zhejiang Province, China. .
Zhang H; Eye Hospital, The First Affiliated Hospital of Harbin Medical University, No.143, Yiman Street, Nangang District, Harbin City, 150001, Heilongjiang Province, China. .; Key Laboratory of Basic and Clinical Research of Heilongjiang Province, Harbin, 150001, China. .; Pokaż więcej
Źródło:: Scientific reports [Sci Rep] 2022 Jan 07; Vol. 12 (1), pp. 264. Date of Electronic Publication: 2022 Jan 07.
Typ publikacji:: Comparative Study; Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms:: Algorithms*
Diagnosis, Computer-Assisted*
Diagnostic Techniques, Ophthalmological*
Image Interpretation, Computer-Assisted*
Conjunctiva/*blood supply
Diabetes Mellitus, Type 2/*pathology
Diabetic Angiopathies/*pathology
Microvessels/*pathology
Case-Control Studies ; Diabetes Mellitus, Type 2/complications ; Diabetic Angiopathies/etiology ; Humans ; Predictive Value of Tests ; Prospective Studies ; Reproducibility of Results

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Skocz do pozycji: 9.

Tytuł:: DUNEScan: a web server for uncertainty estimation in skin cancer detection with deep neural networks.
Autorzy:: Mazoure B; School of Computer Science, McGill University and Quebec AI Institute (MILA), Montreal, Canada. .
Mazoure A; JACOBB Applied Artificial Intelligence Center, Montreal, Canada.
Bédard J; Département d'Informatique, Université du Québec à Montréal, Montreal, Canada.
Makarenkov V; Département d'Informatique, Université du Québec à Montréal, Montreal, Canada.; Pokaż więcej
Źródło:: Scientific reports [Sci Rep] 2022 Jan 07; Vol. 12 (1), pp. 179. Date of Electronic Publication: 2022 Jan 07.
Typ publikacji:: Comparative Study; Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms:: Deep Learning*
Diagnosis, Computer-Assisted*
Image Interpretation, Computer-Assisted*
Internet*
Photography*
Skin Neoplasms/*pathology
Decision Support Techniques ; Humans ; Predictive Value of Tests ; Reproducibility of Results ; Skin Neoplasms/classification ; Uncertainty

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Skocz do pozycji: 10.

Tytuł:: Deep learning for anatomical interpretation of video bronchoscopy images.
Autorzy:: Yoo JY; Department of Anesthesiology and Pain Medicine, Ajou University School of Medicine, Suwon, South Korea.
Kang SY; Department of Anesthesiology and Pain Medicine, Ajou University School of Medicine, Suwon, South Korea.
Park JS; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, South Korea.; Seoul National University College of Medicine, Seoul, South Korea.
Cho YJ; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, South Korea.; Seoul National University College of Medicine, Seoul, South Korea.
Park SY; Department of Anesthesiology and Pain Medicine, Ajou University School of Medicine, Suwon, South Korea.
Yoon HI; Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, South Korea.; Seoul National University College of Medicine, Seoul, South Korea.
Park SJ; Seoul National University College of Medicine, Seoul, South Korea.; Department of Ophthalmology, Seoul National University Bundang Hospital, Seongnam, South Korea.
Jeong HG; Seoul National University College of Medicine, Seoul, South Korea.; Department of Neurosurgery, Seoul National University Bundang Hospital, 82 Gumi-ro 173 beon-gil, Bundang-gu,, Seongnam, 13620, South Korea.; Department of Neurology, Seoul National University Bundang Hospital, Seongnam, South Korea.
Kim T; Seoul National University College of Medicine, Seoul, South Korea. .; Department of Neurosurgery, Seoul National University Bundang Hospital, 82 Gumi-ro 173 beon-gil, Bundang-gu,, Seongnam, 13620, South Korea. .; TALOS Corp., Yongin, South Korea. .; Pokaż więcej
Źródło:: Scientific reports [Sci Rep] 2021 Dec 09; Vol. 11 (1), pp. 23765. Date of Electronic Publication: 2021 Dec 09.
Typ publikacji:: Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms:: Bronchoscopy*/methods
Deep Learning*
Image Interpretation, Computer-Assisted*/methods
Image Processing, Computer-Assisted*/methods
Bronchi/*anatomy & histology
Bronchi/*diagnostic imaging
Anesthesiology/education ; Artificial Intelligence ; Humans ; Reproducibility of Results

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Skocz do pozycji: 11.

Tytuł:: Data-driven identification of diagnostically useful extrastriatal signal in dopamine transporter SPECT using explainable AI.
Autorzy:: Nazari M; Department of Computer Science, Biotech, Technical University Dresden, Dresden, Germany.; ABX-CRO Advanced Pharmaceutical Services Forschungsgesellschaft M.B.H., 01307, Dresden, Germany.
Kluge A; ABX-CRO Advanced Pharmaceutical Services Forschungsgesellschaft M.B.H., 01307, Dresden, Germany.
Apostolova I; Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany.
Klutmann S; Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany.
Kimiaei S; ABX-CRO Advanced Pharmaceutical Services Forschungsgesellschaft M.B.H., 01307, Dresden, Germany.
Schroeder M; Department of Computer Science, Biotech, Technical University Dresden, Dresden, Germany.
Buchert R; Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany. .; Pokaż więcej
Źródło:: Scientific reports [Sci Rep] 2021 Nov 25; Vol. 11 (1), pp. 22932. Date of Electronic Publication: 2021 Nov 25.
Typ publikacji:: Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms:: Image Interpretation, Computer-Assisted*
Neural Networks, Computer*
Tomography, Emission-Computed, Single-Photon*
Brain/*diagnostic imaging
Dopamine Plasma Membrane Transport Proteins/*metabolism
Parkinson Disease/*diagnostic imaging
Radiopharmaceuticals/*administration & dosage
Tropanes/*administration & dosage
Brain/metabolism ; Diagnosis, Differential ; Humans ; Nerve Degeneration ; Parkinson Disease/metabolism ; Predictive Value of Tests ; Radiopharmaceuticals/metabolism ; Reproducibility of Results ; Retrospective Studies ; Tropanes/metabolism

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Skocz do pozycji: 12.

Tytuł:: Effect of data leakage in brain MRI classification using 2D convolutional neural networks.
Autorzy:: Yagis E; School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK.
Atnafu SW; Department of Electrical, Electronic, and Information Engineering 'Guglielmo Marconi', University of Bologna, Via dell'Università 50, 47521, Cesena, Italy.
García Seco de Herrera A; School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK.
Marzi C; Department of Electrical, Electronic, and Information Engineering 'Guglielmo Marconi', University of Bologna, Via dell'Università 50, 47521, Cesena, Italy.
Scheda R; Department of Electrical, Electronic, and Information Engineering 'Guglielmo Marconi', University of Bologna, Via dell'Università 50, 47521, Cesena, Italy.
Giannelli M; Unit of Medical Physics, Pisa University Hospital 'Azienda Ospedaliero-Universitaria Pisana', Pisa, Italy.
Tessa C; Division of Radiology, Versilia Hospital, Azienda USL Toscana Nord Ovest, Lido di Camaiore, LU, Italy.
Citi L; School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK.
Diciotti S; Department of Electrical, Electronic, and Information Engineering 'Guglielmo Marconi', University of Bologna, Via dell'Università 50, 47521, Cesena, Italy. .; Pokaż więcej
Źródło:: Scientific reports [Sci Rep] 2021 Nov 19; Vol. 11 (1), pp. 22544. Date of Electronic Publication: 2021 Nov 19.
Typ publikacji:: Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms:: Image Interpretation, Computer-Assisted*
Magnetic Resonance Imaging*
Neural Networks, Computer*
Neuroimaging*
Alzheimer Disease/*diagnostic imaging
Brain/*diagnostic imaging
Parkinson Disease/*diagnostic imaging
Aged ; Aged, 80 and over ; Case-Control Studies ; Cross-Sectional Studies ; Deep Learning ; Female ; Humans ; Male ; Middle Aged ; Predictive Value of Tests ; Reproducibility of Results

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Skocz do pozycji: 13.

Tytuł:: Quantitative assessment of velocity and flow using compressed SENSE in children and young adults with adequate acquired temporal resolution.
Autorzy:: Kocaoglu M; Department of Radiology, Cincinnati Children's Hospital Medical Center, S1.533, 3333 Burnet Ave, Cincinnati, OH, 45229, USA.; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
Pednekar A; Department of Radiology, Cincinnati Children's Hospital Medical Center, S1.533, 3333 Burnet Ave, Cincinnati, OH, 45229, USA. .; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA. .
Tkach JA; Department of Radiology, Cincinnati Children's Hospital Medical Center, S1.533, 3333 Burnet Ave, Cincinnati, OH, 45229, USA.; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
Taylor MD; The Heart Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.; Pokaż więcej
Źródło:: Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance [J Cardiovasc Magn Reson] 2021 Oct 18; Vol. 23 (1), pp. 113. Date of Electronic Publication: 2021 Oct 18.
Typ publikacji:: Journal Article
MeSH Terms:: Image Interpretation, Computer-Assisted*
Vena Cava, Superior*/diagnostic imaging
Blood Flow Velocity ; Child ; Humans ; Male ; Predictive Value of Tests ; Reproducibility of Results ; Retrospective Studies ; Young Adult

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Skocz do pozycji: 14.

Tytuł:: Weakly supervised learning for classification of lung cytological images using attention-based multiple instance learning.
Autorzy:: Teramoto A; School of Medical Sciences, Fujita Health University, Aichi, Japan. .
Kiriyama Y; School of Medicine, Fujita Health University, Aichi, Japan.
Tsukamoto T; School of Medicine, Fujita Health University, Aichi, Japan.
Sakurai E; School of Medicine, Fujita Health University, Aichi, Japan.
Michiba A; Graduate School of Medicine, Fujita Health University, Aichi, Japan.
Imaizumi K; School of Medicine, Fujita Health University, Aichi, Japan.
Saito K; School of Medical Sciences, Fujita Health University, Aichi, Japan.
Fujita H; Faculty of Engineering, Gifu University, Gifu, Japan.; Pokaż więcej
Źródło:: Scientific reports [Sci Rep] 2021 Oct 13; Vol. 11 (1), pp. 20317. Date of Electronic Publication: 2021 Oct 13.
Typ publikacji:: Journal Article
MeSH Terms:: Deep Learning*
Supervised Machine Learning*
Image Interpretation, Computer-Assisted/*methods
Lung/*diagnostic imaging
Lung Neoplasms/*diagnostic imaging
Adenocarcinoma/diagnostic imaging ; Carcinoma, Squamous Cell/diagnostic imaging ; Chromatin/chemistry ; Humans ; Image Processing, Computer-Assisted/methods ; Medical Informatics/methods ; Neural Networks, Computer ; Pattern Recognition, Automated ; Reproducibility of Results ; Retrospective Studies ; Small Cell Lung Carcinoma/diagnostic imaging ; Thorax

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Skocz do pozycji: 15.

Tytuł:: Improving Ki67 assessment concordance by the use of an artificial intelligence-empowered microscope: a multi-institutional ring study.
Autorzy:: Cai L; Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Yan K; AI Lab, Tencent, Shenzhen, Guangdong, China.
Bu H; Department of Pathology, West China Centre of Medical Sciences, Sichuan University, Chengdu, Sichuan, China.
Yue M; Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Dong P; AI Lab, Tencent, Shenzhen, Guangdong, China.
Wang X; Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Li L; Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Tian K; AI Lab, Tencent, Shenzhen, Guangdong, China.
Shen H; AI Lab, Tencent, Shenzhen, Guangdong, China.
Zhang J; AI Lab, Tencent, Shenzhen, Guangdong, China.
Shang J; Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Niu S; Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Han D; Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Ren C; Department of Pathology, Shenzhou Hospital of Hebei Province, Shenzhou, Hebei, China.
Huang J; AI Lab, Tencent, Shenzhen, Guangdong, China.
Han X; AI Lab, Tencent, Shenzhen, Guangdong, China.
Yao J; AI Lab, Tencent, Shenzhen, Guangdong, China.
Liu Y; Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.; Pokaż więcej
Źródło:: Histopathology [Histopathology] 2021 Oct; Vol. 79 (4), pp. 544-555. Date of Electronic Publication: 2021 Jun 24.
Typ publikacji:: Journal Article; Multicenter Study
MeSH Terms:: Artificial Intelligence*
Biomarkers, Tumor/*analysis
Breast Neoplasms/*diagnosis
Image Interpretation, Computer-Assisted/*methods
Ki-67 Antigen/*analysis
Microscopy/*methods
Female ; Humans ; Image Interpretation, Computer-Assisted/instrumentation ; Microscopy/instrumentation ; Observer Variation ; Pathology, Clinical/instrumentation ; Pathology, Clinical/methods ; Reproducibility of Results ; Retrospective Studies

Czasopismo naukowe

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Skocz do pozycji: 16.

Tytuł:: Reproducibility of left ventricular blood flow kinetic energy measured by four-dimensional flow CMR.
Autorzy:: Grafton-Clarke C; George Davies Centre, School of Medicine, University of Leicester, Lancaster Road, Leicester, LE1 7HA, UK. .
Crandon S; Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK.
Westenberg JJM; Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
Swoboda PP; Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK.
Greenwood JP; Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK.
van der Geest RJ; Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
Swift AJ; Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield, UK.
Vassiliou VS; Norwich Medical School, University of East Anglia, Norwich, UK.
Plein S; Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK.
Garg P; Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield, UK.; Norwich Medical School, University of East Anglia, Norwich, UK.; Pokaż więcej
Źródło:: BMC research notes [BMC Res Notes] 2021 Jul 27; Vol. 14 (1), pp. 289. Date of Electronic Publication: 2021 Jul 27.
Typ publikacji:: Journal Article
MeSH Terms:: Image Interpretation, Computer-Assisted*
Blood Flow Velocity ; Humans ; Netherlands ; Predictive Value of Tests ; Reproducibility of Results ; United Kingdom

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na półce

Pełny tekst Pełny tekst Szczegóły

Skocz do pozycji: 17.

Tytuł:: Digital Hologram Watermarking Based on Multiple Deep Neural Networks Training Reconstruction and Attack.
Autorzy:: Kang JW; Department of Electronic Materials Engeering, Kwangwoon University, Kwangwoon-ro 20, Nowon-gu, Seoul 01897, Korea.
Lee JE; OLED Team Associate, Siliconworks, Baumoe-ro, Seocho-gu, Seoul 06763, Korea.
Choi JH; Department of Electronic Materials Engeering, Kwangwoon University, Kwangwoon-ro 20, Nowon-gu, Seoul 01897, Korea.
Kim W; Department of Electronic Materials Engeering, Kwangwoon University, Kwangwoon-ro 20, Nowon-gu, Seoul 01897, Korea.
Kim JK; Department of Electronic Materials Engeering, Kwangwoon University, Kwangwoon-ro 20, Nowon-gu, Seoul 01897, Korea.
Kim DW; Department of Electronic Materials Engeering, Kwangwoon University, Kwangwoon-ro 20, Nowon-gu, Seoul 01897, Korea.
Seo YH; Department of Electronic Materials Engeering, Kwangwoon University, Kwangwoon-ro 20, Nowon-gu, Seoul 01897, Korea.; Pokaż więcej
Źródło:: Sensors (Basel, Switzerland) [Sensors (Basel)] 2021 Jul 22; Vol. 21 (15). Date of Electronic Publication: 2021 Jul 22.
Typ publikacji:: Journal Article
MeSH Terms:: Computer Security*
Image Interpretation, Computer-Assisted*
Algorithms ; Neural Networks, Computer ; Reproducibility of Results

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na półce

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Skocz do pozycji: 18.

Tytuł:: Deep learning for classification of pediatric chest radiographs by WHO's standardized methodology.
Autorzy:: Chen Y; Merck & Co., Inc., Kenilworth, New Jersey, United States of America.
Roberts CS; Merck & Co., Inc., Kenilworth, New Jersey, United States of America.
Ou W; Merck & Co., Inc., Kenilworth, New Jersey, United States of America.
Petigara T; Merck & Co., Inc., Kenilworth, New Jersey, United States of America.
Goldmacher GV; Merck & Co., Inc., Kenilworth, New Jersey, United States of America.
Fancourt N; Menzies School of Health Research, Charles Darwin University, Darwin, Australia.
Knoll MD; Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.; Pokaż więcej
Źródło:: PloS one [PLoS One] 2021 Jun 21; Vol. 16 (6), pp. e0253239. Date of Electronic Publication: 2021 Jun 21 (Print Publication: 2021).
Typ publikacji:: Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms:: Deep Learning*
Image Interpretation, Computer-Assisted/*methods
Radiography, Thoracic/*classification
Datasets as Topic ; Female ; Humans ; Infant ; Male ; Models, Statistical ; Observer Variation ; Pneumonia/classification ; Pneumonia/diagnostic imaging ; ROC Curve ; Reproducibility of Results ; World Health Organization

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Skocz do pozycji: 19.

Tytuł:: Radiomics side experiments and DAFIT approach in identifying pulmonary hypertension using Cardiac MRI derived radiomics based machine learning models.
Autorzy:: Priya S; Department of Radiology, University of Iowa Carver College of Medicine, 200 Hawkins Dr, Iowa City, IA, 52242, USA. .
Aggarwal T; Department of Family Medicine, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA.
Ward C; Department of Biostatistics, University of Iowa College of Public Health, Iowa City, IA, USA.
Bathla G; Department of Radiology, University of Iowa Carver College of Medicine, 200 Hawkins Dr, Iowa City, IA, 52242, USA.
Jacob M; Department of Electrical Engineering, University of Iowa College of Engineering, Iowa City, IA, USA.
Gerke A; Department of Pulmonary Medicine, University of Iowa Carver College of Medicine, Iowa City, , IA, USA.
Hoffman EA; Department of Radiology, University of Iowa Carver College of Medicine, 200 Hawkins Dr, Iowa City, IA, 52242, USA.; Roy J. Carver Department of Biomedical Engineering, University of Iowa College of Engineering, Iowa City, IA, USA.
Nagpal P; Department of Radiology, University of Iowa Carver College of Medicine, 200 Hawkins Dr, Iowa City, IA, 52242, USA.; Pokaż więcej
Źródło:: Scientific reports [Sci Rep] 2021 Jun 16; Vol. 11 (1), pp. 12686. Date of Electronic Publication: 2021 Jun 16.
Typ publikacji:: Journal Article; Research Support, Non-U.S. Gov't
MeSH Terms:: Image Interpretation, Computer-Assisted*
Machine Learning*
Magnetic Resonance Imaging*
Heart/*diagnostic imaging
Hypertension, Pulmonary/*diagnostic imaging
Female ; Humans ; Hypertension, Pulmonary/diagnosis ; Male ; Reproducibility of Results ; Retrospective Studies

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Skocz do pozycji: 20.

Tytuł:: Automatic classification of medical image modality and anatomical location using convolutional neural network.
Autorzy:: Chiang CH; Department of Radiology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.; Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
Weng CL; Tainan Municipal Hospital, Tainan, Taiwan.
Chiu HW; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.; Pokaż więcej
Źródło:: PloS one [PLoS One] 2021 Jun 11; Vol. 16 (6), pp. e0253205. Date of Electronic Publication: 2021 Jun 11 (Print Publication: 2021).
Typ publikacji:: Journal Article
MeSH Terms:: Image Interpretation, Computer-Assisted*
Neural Networks, Computer*
Abdomen/diagnostic imaging ; Automation/methods ; Brain/diagnostic imaging ; Humans ; Magnetic Resonance Imaging/methods ; Neuroimaging/methods ; Reproducibility of Results ; Sensitivity and Specificity ; Spine/diagnostic imaging ; Tomography, X-Ray Computed/methods

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