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Daniel L Rubin

Showing results (31-40 of 261) with videos related to

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Journal of Digital Imaging|April 16, 2011
Integration of imaging signs into RadLexMatthew W Shore, Daniel L Rubin, Charles E Kahn
Journal of Digital Imaging|October 11, 2017
Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical ImagesSebastian Echegaray, Shaimaa Bakr, Daniel L Rubin, et al.
Academic Radiology|January 24, 2006
Coverage of emergency after-hours ultrasound cases: survey of practices at U.S. Teaching hospitalsTerry S Desser, Daniel L Rubin, Pamela Schraedley-Desmond
IEEE Transactions on Medical Imaging|January 24, 2017
Adaptive Estimation of Active Contour Parameters Using Convolutional Neural Networks and Texture AnalysisAssaf Hoogi, Arjun Subramaniam, Rishi Veerapaneni, et al.
Journal of Digital Imaging|June 10, 2006
An ontology for PACS integrationCharles E Kahn, David S Channin, Daniel L Rubin
AMIA ... Annual Symposium Proceedings. AMIA Symposium|November 13, 2008
iPad: Semantic annotation and markup of radiological imagesDaniel L Rubin, Cesar Rodriguez, Priyanka Shah, et al.
Journal of Digital Imaging|June 22, 2019
Automated Detection of Measurements and Their Descriptors in Radiology Reports Using a Hybrid Natural Language Processing AlgorithmSelen Bozkurt, Emel Alkim, Imon Banerjee, et al.
Journal of the American Medical Informatics Association : JAMIA|March 21, 2020
Accounting for data variability in multi-institutional distributed deep learning for medical imagingNiranjan Balachandar, Ken Chang, Jayashree Kalpathy-Cramer, et al.
Journal of Biomedical Informatics|February 3, 2016
Toward rapid learning in cancer treatment selection: An analytical engine for practice-based clinical dataSamuel G Finlayson, Mia Levy, Sunil Reddy, et al.
Journal of the American Medical Informatics Association : JAMIA|June 29, 2020
Corrigendum to: Accounting for data variability in multi-institutional distributed deep learning for medical imagingNiranjan Balachandar, Ken Chang, Jayashree Kalpathy-Cramer, et al.
Pageof 27

Showing results (31-40 of 261) with videos related to

Sort By:
Pageof 27
Journal of Digital Imaging|April 16, 2011
Integration of imaging signs into RadLexMatthew W Shore, Daniel L Rubin, Charles E Kahn
Journal of Digital Imaging|October 11, 2017
Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical ImagesSebastian Echegaray, Shaimaa Bakr, Daniel L Rubin, et al.
Academic Radiology|January 24, 2006
Coverage of emergency after-hours ultrasound cases: survey of practices at U.S. Teaching hospitalsTerry S Desser, Daniel L Rubin, Pamela Schraedley-Desmond
IEEE Transactions on Medical Imaging|January 24, 2017
Adaptive Estimation of Active Contour Parameters Using Convolutional Neural Networks and Texture AnalysisAssaf Hoogi, Arjun Subramaniam, Rishi Veerapaneni, et al.
Journal of Digital Imaging|June 10, 2006
An ontology for PACS integrationCharles E Kahn, David S Channin, Daniel L Rubin
AMIA ... Annual Symposium Proceedings. AMIA Symposium|November 13, 2008
iPad: Semantic annotation and markup of radiological imagesDaniel L Rubin, Cesar Rodriguez, Priyanka Shah, et al.
Journal of Digital Imaging|June 22, 2019
Automated Detection of Measurements and Their Descriptors in Radiology Reports Using a Hybrid Natural Language Processing AlgorithmSelen Bozkurt, Emel Alkim, Imon Banerjee, et al.
Journal of the American Medical Informatics Association : JAMIA|March 21, 2020
Accounting for data variability in multi-institutional distributed deep learning for medical imagingNiranjan Balachandar, Ken Chang, Jayashree Kalpathy-Cramer, et al.
Journal of Biomedical Informatics|February 3, 2016
Toward rapid learning in cancer treatment selection: An analytical engine for practice-based clinical dataSamuel G Finlayson, Mia Levy, Sunil Reddy, et al.
Journal of the American Medical Informatics Association : JAMIA|June 29, 2020
Corrigendum to: Accounting for data variability in multi-institutional distributed deep learning for medical imagingNiranjan Balachandar, Ken Chang, Jayashree Kalpathy-Cramer, et al.
Pageof 27