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Jeffrey De Fauw

Showing results (1-10 of 8) with videos related to

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F1000Research|July 8, 2017
Automated analysis of retinal imaging using machine learning techniques for computer visionJeffrey De Fauw, Pearse Keane, Nenad Tomasev, et al.
Nature Medicine|June 10, 2020
Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: The STARD-AI Steering GroupViknesh Sounderajah, Hutan Ashrafian, Ravi Aggarwal, et al.
Nature Medicine|May 20, 2020
Predicting conversion to wet age-related macular degeneration using deep learningJason Yim, Reena Chopra, Terry Spitz, et al.
Journal of Medical Internet Research|July 13, 2021
Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation StudyStanislav Nikolov, Sam Blackwell, Alexei Zverovitch, et al.
BMJ Open|June 29, 2021
Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocolViknesh Sounderajah, Hutan Ashrafian, Robert M Golub, et al.
Nature|October 15, 2020
Addendum: International evaluation of an AI system for breast cancer screeningScott Mayer McKinney, Marcin Sieniek, Varun Godbole, et al.
Nature|January 3, 2020
International evaluation of an AI system for breast cancer screeningScott Mayer McKinney, Marcin Sieniek, Varun Godbole, et al.
Nature Medicine|August 15, 2018
Clinically applicable deep learning for diagnosis and referral in retinal diseaseJeffrey De Fauw, Joseph R Ledsam, Bernardino Romera-Paredes, et al.
Pageof 1

Showing results (1-10 of 8) with videos related to

Sort By:
Pageof 1
F1000Research|July 8, 2017
Automated analysis of retinal imaging using machine learning techniques for computer visionJeffrey De Fauw, Pearse Keane, Nenad Tomasev, et al.
Nature Medicine|June 10, 2020
Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: The STARD-AI Steering GroupViknesh Sounderajah, Hutan Ashrafian, Ravi Aggarwal, et al.
Nature Medicine|May 20, 2020
Predicting conversion to wet age-related macular degeneration using deep learningJason Yim, Reena Chopra, Terry Spitz, et al.
Journal of Medical Internet Research|July 13, 2021
Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation StudyStanislav Nikolov, Sam Blackwell, Alexei Zverovitch, et al.
BMJ Open|June 29, 2021
Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocolViknesh Sounderajah, Hutan Ashrafian, Robert M Golub, et al.
Nature|October 15, 2020
Addendum: International evaluation of an AI system for breast cancer screeningScott Mayer McKinney, Marcin Sieniek, Varun Godbole, et al.
Nature|January 3, 2020
International evaluation of an AI system for breast cancer screeningScott Mayer McKinney, Marcin Sieniek, Varun Godbole, et al.
Nature Medicine|August 15, 2018
Clinically applicable deep learning for diagnosis and referral in retinal diseaseJeffrey De Fauw, Joseph R Ledsam, Bernardino Romera-Paredes, et al.
Pageof 1