검색어 : 통합검색[Introduction to deep learning :]
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911.
- Algorithms for Collaborative Machine Learning under Statistical Heterogeneity
- Seok-Ju Hahn
- Ulsan National Institute of Science and Technology, 국내박사, 151, 2024
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912
- Prediction of Stroke Infarct Growth Rates by Baseline Perfusion Imaging
- Wouters, Anke; Robben, David; Christensen, Soren; Marquering, Henk A.; Roos, Yvo B.W.E.M.; van Oostenbrugge, Robert J.; van Zwam, Wim H.; Dippel, Diederik W.J.; Majoie, Charles B.L.M.; Schonewille, Wouter J.; van der Lugt, Aad; Lansberg, Maarten; Albers, Gregory W.; Suetens, Paul; Lemmens, Robin; Department of Neurology, University Hospitals Leuven, Belgium (A.W., R.L.).; Medical Imaging Research Center (MIRC), KU Leuven, Belgium (D.R., P.S.).; GrayNumber Analytics, Lomma, Sweden (S.C.).; Department of Radiology and Nuclear Medicine, Academic Medical Center, Amsterdam, the Netherlands (H.A.M., C.B.L.M.M.).; Department of Neurology, Academic Medical Center, the Netherlands (A.W., Y.B.W.E.M.R.).; Department of Neurology, Maastricht University Medical Center and Cardiovascular Research Institute (CARIM), the Netherlands (R.J.v.O.).; Department of Radiology, Maastricht University Medical Center and Cardiovascular Research Institute (CARIM), the Netherlands (W.H.v.Z.).; Department of Neurology, Erasmus MC University Medical Center, Rotterdam, Netherlands (D.W.J.D.).; Department of Radiology and Nuclear Medicine, Academic Medical Center, Amsterdam, the Netherlands (H.A.M., C.B.L.M.M.).; Department of Neurology, St. Antonius Hospital, Nieuwegein, and University Medical Center; (Stroke, v.53, 2022, pp.569-577)
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913
- Abstract P333: Prediction of Stroke Lesion Growth Rates by Baseline Perfusion Imaging
- Wouters, Anke; Robben, David; Christensen, Soren; Marquering, Henk; Roos, Yvo; Oostenbrugge, Robert Van V; van Zwam, Wim; DIPPEL, Diederik W; Majoie, Charles B; van der Lugt, Aad; Lansberg, Maarten G; Albers, Gregory W; Suetens, Paul; Lemmens, Robin; UZ LEUVEN NEUROLOGY, Leuven, Belgium; KU Leuven, Leuven, Belgium; Stanford Stroke center, Stanford, CA; Amsterdam; Amsterdam UMC, Amsterdam; UNIV HOSPITAL MAASTRICHT, Maastricht; MUMC, Maastricht; ERASMUS MC, Rotterdam; AMC, Amsterdam; Erasmus Univ; STANFORD UNIVERSITY, Palo Alto, CA; Stanford Univ Med Cntr, Stanford, CA; KU Leuven, Leuven, Belgium; UNIVERSITY HOSPITALS LEUVEN, Leuven, Belgium; (Stroke, v.52, 2021, )
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914
- Abstract 13588: A Generalizable Deep Learning System for Cardiac MRI
- Shad, Rohan; Zakka, Cyril R; Kaur, Dhamanpreet; MONGAN, JOHN; Kallianos, Kimberly G; Filice, Ross; Khandwala, Nishith; Eng, David; Langlotz, Curtis; Hiesinger, William; Cardiovascular Surgery, Univ of Pennsylvania, Philadelphia, PA; Cardiothoracic Surgery, Stanford Univ, Stanford, CA; Cardiovascular Surgery, Stanford Univ, Stanford, CA; Radiology, UCSF, San Francisco, CA; UCSF, San Francisco, CA; Radiology, MedStar Georgetown Univ Hosp, Waldorf, MD; Bunkerhill Health, Palo Alto, CA; Bunkerhill Health, Palo Alto, CA; Radiology, Biomedical Informatics, Biomedical Data Science, Stanford Univ, Stanford, CA; Dept of Cardiothoracic Surgery, Stanford Univ, Stanford, CA; (Circulation, v.148, 2023, )
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915
- A review and experimental evaluation of deep learning methods for MRI reconstruction
- Pal, Arghya; Rathi, Yogesh; Harvard Medical School; Harvard Medical School; (The journal of machine learning for biomedical imaging, v.1, 2022, pp.1-50)
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916.
- DeepAttend : 전자 건강 기록을 통해 임상 사건을 예측하기위한 심도 깊은주의 메커니즘.
- 가스파드
- 상명대학교 일반대학원, 국내박사, 122, 2020
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917
- Deep learning for natural language processing of free-text pathology reports: a comparison of learning curves
- Senders, Joeky T; Cote, David J; Mehrtash, Alireza; Wiemann, Robert; Gormley, William B; Smith, Timothy R; Broekman, Marike L D; Arnaout, Omar; Department of Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands; Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA; Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA; Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA; Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA; Neurosurgery, Haaglanden Medisch Centrum, Den Haag, Zuid-Holland, The Netherlands; Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA; (BMJ innovations, v.6, 2020, pp.192-198)
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918
- Object Detection With Deep Learning: A Review
- Zhao, Zhong-Qiu; Zheng, Peng; Xu, Shou-Tao; Wu, Xindong; Hefei University of Technology, Hefei, China; Hefei University of Technology, Hefei, China; Hefei University of Technology, Hefei, China; University of Louisiana at Lafayette, Lafayette, LA, USA; (IEEE transactions on neural networks and learning systems, v.30, 2019, pp.3212-3232)
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919
- 875 Using Stress Testing to Identify Vulnerabilities in Artificial Intelligence Models for the Identification of Culprit Carotid Lesions in Cerebrovascular Events
- Le, E; Tarkin, J; Evans, N; Chowdhury, M; Rudd, J; Department of Medicine, Addenbrooke's Hospital, Cambridge, United Kingdom; Department of Medicine, Addenbrooke's Hospital, Cambridge, United Kingdom; Department of Medicine, Addenbrooke's Hospital, Cambridge, United Kingdom; Division of Vascular and Endovascular Surgery, Addenbrooke's Hospital, Cambridge, United Kingdom; Department of Medicine, Addenbrooke's Hospital, Cambridge, United Kingdom; (British journal of surgery : BJS, v.108, 2021, pp.znab259.1123)
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920
- Learning Approach and Trait Anxiety on Paramedic and Nursing Students
- Uzuntarla, Yasin; Gulhane Training and Research Hospital, Ministry of Health, Turkey; (Australasian journal of paramedicine, v.15, 2018, pp.1-6)
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