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Showing results (31-40 of 100) with videos related to

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Skin Health and Disease|June 6, 2022
Machine learning for clinical operations improvement via case triagingS J Huang, Y Liu, K Kanada, et al.
Thrombosis and Haemostasis|February 24, 2001
High levels of adhesion molecules are associated with impaired endothelium-dependent vasodilation in patients with peripheral arterial diseaseG Brevetti, V D Martone, T de Cristofaro, et al.
Nature Biomedical Engineering|April 25, 2019
Prediction of cardiovascular risk factors from retinal fundus photographs via deep learningRyan Poplin, Avinash V Varadarajan, Katy Blumer, et al.
Cytometry. Part B, Clinical Cytometry|February 14, 2006
Increased immature hematopoietic progenitor cells CD34+/CD38dim in myelodysplasiaMariela B Monreal, Maria L Pardo, Miguel A Pavlovsky, et al.
Nature Biomedical Engineering|December 25, 2019
Detection of anaemia from retinal fundus images via deep learningAkinori Mitani, Abigail Huang, Subhashini Venugopalan, et al.
Nature Biomedical Engineering|February 14, 2020
Author Correction: Detection of anaemia from retinal fundus images via deep learningAkinori Mitani, Abigail Huang, Subhashini Venugopalan, et al.
Medicina|June 3, 2000
[Eight years of experience in a single institution in hematopoietic stem cell autologous transplantation in malignant hematological diseases and in solid tumors]G A Milone, J Martínez Rolón, I I Fernández, et al.
Surgical Endoscopy|August 8, 2022
Artificial intelligence for phase recognition in complex laparoscopic cholecystectomyTomer Golany, Amit Aides, Daniel Freedman, et al.
Journal of Endocrinological Investigation|June 24, 2006
Value of routine measurement of serum calcitonin concentrations in patients with nodular thyroid disease: A multicenter studyG Papi, S M Corsello, K Cioni, et al.
Investigative Ophthalmology & Visual Science|July 20, 2018
Deep Learning for Predicting Refractive Error From Retinal Fundus ImagesAvinash V Varadarajan, Ryan Poplin, Katy Blumer, et al.
Pageof 10

Showing results (31-40 of 100) with videos related to

Sort By:
Pageof 10
Skin Health and Disease|June 6, 2022
Machine learning for clinical operations improvement via case triagingS J Huang, Y Liu, K Kanada, et al.
Thrombosis and Haemostasis|February 24, 2001
High levels of adhesion molecules are associated with impaired endothelium-dependent vasodilation in patients with peripheral arterial diseaseG Brevetti, V D Martone, T de Cristofaro, et al.
Nature Biomedical Engineering|April 25, 2019
Prediction of cardiovascular risk factors from retinal fundus photographs via deep learningRyan Poplin, Avinash V Varadarajan, Katy Blumer, et al.
Cytometry. Part B, Clinical Cytometry|February 14, 2006
Increased immature hematopoietic progenitor cells CD34+/CD38dim in myelodysplasiaMariela B Monreal, Maria L Pardo, Miguel A Pavlovsky, et al.
Nature Biomedical Engineering|December 25, 2019
Detection of anaemia from retinal fundus images via deep learningAkinori Mitani, Abigail Huang, Subhashini Venugopalan, et al.
Nature Biomedical Engineering|February 14, 2020
Author Correction: Detection of anaemia from retinal fundus images via deep learningAkinori Mitani, Abigail Huang, Subhashini Venugopalan, et al.
Medicina|June 3, 2000
[Eight years of experience in a single institution in hematopoietic stem cell autologous transplantation in malignant hematological diseases and in solid tumors]G A Milone, J Martínez Rolón, I I Fernández, et al.
Surgical Endoscopy|August 8, 2022
Artificial intelligence for phase recognition in complex laparoscopic cholecystectomyTomer Golany, Amit Aides, Daniel Freedman, et al.
Journal of Endocrinological Investigation|June 24, 2006
Value of routine measurement of serum calcitonin concentrations in patients with nodular thyroid disease: A multicenter studyG Papi, S M Corsello, K Cioni, et al.
Investigative Ophthalmology & Visual Science|July 20, 2018
Deep Learning for Predicting Refractive Error From Retinal Fundus ImagesAvinash V Varadarajan, Ryan Poplin, Katy Blumer, et al.
Pageof 10