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Ophthalmology
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December 23, 2019
Using Deep Learning Models to Characterize Major Retinal Features on Color Fundus Photographs
Cecilia 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 Ophthalmology
Cecilia S Lee, James D Brandt, Aaron Y Lee
Ophthalmology
|
June 21, 2024
Leveraging Real-World Evidence to Enhance Clinical Trials
Durga 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 Degeneration
Cecilia S Lee, Doug M Baughman, Aaron Y Lee
JAMA Ophthalmology
|
September 13, 2019
Finding Glaucoma in Color Fundus Photographs Using Deep Learning
Karine 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 Go
Sally L Baxter, Kristen Nwanyanwu, Gary Legault, et al.
Nature Medicine
|
April 30, 2025
Promoting transparency in AI for biomedical and behavioral research
Tina 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 populations
Aaron Y Lee, Cecilia S Lee, Russell N Van Gelder
Ophthalmology
|
July 24, 2017
Reply
Cecilia S Lee, Russell N Van Gelder, Aaron Y Lee
Page
of 22
Search research articles
Search
Showing results (21-30 of 212) with videos related to
Sort By:
Page
of 22
Ophthalmology
|
December 23, 2019
Using Deep Learning Models to Characterize Major Retinal Features on Color Fundus Photographs
Cecilia 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 Ophthalmology
Cecilia S Lee, James D Brandt, Aaron Y Lee
Ophthalmology
|
June 21, 2024
Leveraging Real-World Evidence to Enhance Clinical Trials
Durga 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 Degeneration
Cecilia S Lee, Doug M Baughman, Aaron Y Lee
JAMA Ophthalmology
|
September 13, 2019
Finding Glaucoma in Color Fundus Photographs Using Deep Learning
Karine 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 Go
Sally L Baxter, Kristen Nwanyanwu, Gary Legault, et al.
Nature Medicine
|
April 30, 2025
Promoting transparency in AI for biomedical and behavioral research
Tina 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 populations
Aaron Y Lee, Cecilia S Lee, Russell N Van Gelder
Ophthalmology
|
July 24, 2017
Reply
Cecilia S Lee, Russell N Van Gelder, Aaron Y Lee
Page
of 22