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Aaron Y Lee

Showing results (21-30 of 212) with videos related to

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Ophthalmology|December 23, 2019
Using Deep Learning Models to Characterize Major Retinal Features on Color Fundus PhotographsCecilia S Lee, Ryan T Yanagihara, Aaron Y Lee
Ophthalmology Science|October 17, 2022
Big Data and Artificial Intelligence in Ophthalmology: Where Are We Now?Cecilia S Lee, James D Brandt, Aaron Y Lee
Ophthalmology Science|February 9, 2024
Entering the Exciting Era of Artificial Intelligence and Big Data in OphthalmologyCecilia S Lee, James D Brandt, Aaron Y Lee
Ophthalmology|June 21, 2024
Leveraging Real-World Evidence to Enhance Clinical TrialsDurga S Borkar, David W Parke, Aaron Y Lee
Ophthalmology. Retina|January 30, 2019
Deep learning is effective for the classification of OCT images of normal versus Age-related Macular DegenerationCecilia S Lee, Doug M Baughman, Aaron Y Lee
JAMA Ophthalmology|September 13, 2019
Finding Glaucoma in Color Fundus Photographs Using Deep LearningKarine D Bojikian, Cecilia S Lee, Aaron Y Lee
Ophthalmology|September 4, 2022
Data Sources for Evaluating Health Disparities in Ophthalmology: Where We Are and Where We Need to GoSally L Baxter, Kristen Nwanyanwu, Gary Legault, et al.
Nature Medicine|April 30, 2025
Promoting transparency in AI for biomedical and behavioral researchTina Hernandez-Boussard, Aaron Y Lee, Julia Stoyanovich, et al.
BMC Bioinformatics|July 29, 2016
Scalable metagenomics alignment research tool (SMART): a scalable, rapid, and complete search heuristic for the classification of metagenomic sequences from complex sequence populationsAaron Y Lee, Cecilia S Lee, Russell N Van Gelder
Ophthalmology|July 24, 2017
ReplyCecilia S Lee, Russell N Van Gelder, Aaron Y Lee
Pageof 22

Showing results (21-30 of 212) with videos related to

Sort By:
Pageof 22
Ophthalmology|December 23, 2019
Using Deep Learning Models to Characterize Major Retinal Features on Color Fundus PhotographsCecilia S Lee, Ryan T Yanagihara, Aaron Y Lee
Ophthalmology Science|October 17, 2022
Big Data and Artificial Intelligence in Ophthalmology: Where Are We Now?Cecilia S Lee, James D Brandt, Aaron Y Lee
Ophthalmology Science|February 9, 2024
Entering the Exciting Era of Artificial Intelligence and Big Data in OphthalmologyCecilia S Lee, James D Brandt, Aaron Y Lee
Ophthalmology|June 21, 2024
Leveraging Real-World Evidence to Enhance Clinical TrialsDurga S Borkar, David W Parke, Aaron Y Lee
Ophthalmology. Retina|January 30, 2019
Deep learning is effective for the classification of OCT images of normal versus Age-related Macular DegenerationCecilia S Lee, Doug M Baughman, Aaron Y Lee
JAMA Ophthalmology|September 13, 2019
Finding Glaucoma in Color Fundus Photographs Using Deep LearningKarine D Bojikian, Cecilia S Lee, Aaron Y Lee
Ophthalmology|September 4, 2022
Data Sources for Evaluating Health Disparities in Ophthalmology: Where We Are and Where We Need to GoSally L Baxter, Kristen Nwanyanwu, Gary Legault, et al.
Nature Medicine|April 30, 2025
Promoting transparency in AI for biomedical and behavioral researchTina Hernandez-Boussard, Aaron Y Lee, Julia Stoyanovich, et al.
BMC Bioinformatics|July 29, 2016
Scalable metagenomics alignment research tool (SMART): a scalable, rapid, and complete search heuristic for the classification of metagenomic sequences from complex sequence populationsAaron Y Lee, Cecilia S Lee, Russell N Van Gelder
Ophthalmology|July 24, 2017
ReplyCecilia S Lee, Russell N Van Gelder, Aaron Y Lee
Pageof 22