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Showing results (171-180 of 507) with videos related to

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IEEE Transactions on Bio-Medical Engineering|May 16, 2013
Quantitative analysis of superparamagnetic contrast agent in sentinel lymph nodes using ex vivo vibrating sample magnetometryMartijn Visscher, Joost J Pouw, Joop van Baarlen, et al.
Medical Physics|March 21, 2019
Combining handcrafted features with latent variables in machine learning for prediction of radiation-induced lung damageSunan Cui, Yi Luo, Huan-Hsin Tseng, et al.
Medical Physics|May 18, 2020
Introduction to machine and deep learning for medical physicistsSunan Cui, Huan-Hsin Tseng, Julia Pakela, et al.
Scientific Reports|February 9, 2023
Author Correction: Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapyDipesh Niraula, Jamalina Jamaluddin, Martha M Matuszak, et al.
Physics in Medicine and Biology|August 4, 2005
An application of Bayesian statistical methods to adaptive radiotherapyKwok L Lam, Randall K Ten Haken, Dale Litzenberg, et al.
Nursing in Critical Care|May 20, 2017
Survey among critical care nurses and physicians about delirium managementPeter Nydahl, Michael Dewes, Rolf Dubb, et al.
Pediatric Critical Care Medicine : a Journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies|July 31, 2018
Cognitive and Behavioral Consequences of Pediatric Delirium: A Pilot StudyJochen Meyburg, Markus Ries, Matthias Zielonka, et al.
JCO Clinical Cancer Informatics|January 8, 2019
Prospects and challenges for clinical decision support in the era of big dataIssam El Naqa, Michael R Kosorok, Judy Jin, et al.
Proceedings of the National Academy of Sciences of the United States of America|May 14, 2004
Microtesla MRI with a superconducting quantum interference deviceRobert McDermott, SeungKyun Lee, Bennie ten Haken, et al.
Scientific Reports|December 8, 2021
Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapyDipesh Niraula, Jamalina Jamaluddin, Martha M Matuszak, et al.
Pageof 51

Showing results (171-180 of 507) with videos related to

Sort By:
Pageof 51
IEEE Transactions on Bio-Medical Engineering|May 16, 2013
Quantitative analysis of superparamagnetic contrast agent in sentinel lymph nodes using ex vivo vibrating sample magnetometryMartijn Visscher, Joost J Pouw, Joop van Baarlen, et al.
Medical Physics|March 21, 2019
Combining handcrafted features with latent variables in machine learning for prediction of radiation-induced lung damageSunan Cui, Yi Luo, Huan-Hsin Tseng, et al.
Medical Physics|May 18, 2020
Introduction to machine and deep learning for medical physicistsSunan Cui, Huan-Hsin Tseng, Julia Pakela, et al.
Scientific Reports|February 9, 2023
Author Correction: Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapyDipesh Niraula, Jamalina Jamaluddin, Martha M Matuszak, et al.
Physics in Medicine and Biology|August 4, 2005
An application of Bayesian statistical methods to adaptive radiotherapyKwok L Lam, Randall K Ten Haken, Dale Litzenberg, et al.
Nursing in Critical Care|May 20, 2017
Survey among critical care nurses and physicians about delirium managementPeter Nydahl, Michael Dewes, Rolf Dubb, et al.
Pediatric Critical Care Medicine : a Journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies|July 31, 2018
Cognitive and Behavioral Consequences of Pediatric Delirium: A Pilot StudyJochen Meyburg, Markus Ries, Matthias Zielonka, et al.
JCO Clinical Cancer Informatics|January 8, 2019
Prospects and challenges for clinical decision support in the era of big dataIssam El Naqa, Michael R Kosorok, Judy Jin, et al.
Proceedings of the National Academy of Sciences of the United States of America|May 14, 2004
Microtesla MRI with a superconducting quantum interference deviceRobert McDermott, SeungKyun Lee, Bennie ten Haken, et al.
Scientific Reports|December 8, 2021
Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapyDipesh Niraula, Jamalina Jamaluddin, Martha M Matuszak, et al.
Pageof 51