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Katia D Pacheco

Showing results (1-10 of 7) with videos related to

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American Journal of Ophthalmology|February 19, 2017
Evaluation of Macular Vascular Abnormalities Identified by Optical Coherence Tomography Angiography in Sickle Cell DiseaseIan C Han, Mongkol Tadarati, Katia D Pacheco, et al.
Translational Vision Science & Technology|May 18, 2021
Addressing Artificial Intelligence Bias in Retinal DiagnosticsPhilippe Burlina, Neil Joshi, William Paul, et al.
Computers in Biology and Medicine|February 8, 2017
Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysisPhilippe Burlina, Katia D Pacheco, Neil Joshi, et al.
JAMA Ophthalmology|September 4, 2020
Low-Shot Deep Learning of Diabetic Retinopathy With Potential Applications to Address Artificial Intelligence Bias in Retinal Diagnostics and Rare Ophthalmic DiseasesPhilippe Burlina, William Paul, Philip Mathew, et al.
JAMA Ophthalmology|January 11, 2019
Assessment of Deep Generative Models for High-Resolution Synthetic Retinal Image Generation of Age-Related Macular DegenerationPhilippe M Burlina, Neil Joshi, Katia D Pacheco, et al.
JAMA Ophthalmology|September 23, 2018
Use of Deep Learning for Detailed Severity Characterization and Estimation of 5-Year Risk Among Patients With Age-Related Macular DegenerationPhilippe M Burlina, Neil Joshi, Katia D Pacheco, et al.
JAMA Ophthalmology|October 4, 2017
Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural NetworksPhilippe M Burlina, Neil Joshi, Michael Pekala, et al.
Pageof 1

Showing results (1-10 of 7) with videos related to

Sort By:
Pageof 1
American Journal of Ophthalmology|February 19, 2017
Evaluation of Macular Vascular Abnormalities Identified by Optical Coherence Tomography Angiography in Sickle Cell DiseaseIan C Han, Mongkol Tadarati, Katia D Pacheco, et al.
Translational Vision Science & Technology|May 18, 2021
Addressing Artificial Intelligence Bias in Retinal DiagnosticsPhilippe Burlina, Neil Joshi, William Paul, et al.
Computers in Biology and Medicine|February 8, 2017
Comparing humans and deep learning performance for grading AMD: A study in using universal deep features and transfer learning for automated AMD analysisPhilippe Burlina, Katia D Pacheco, Neil Joshi, et al.
JAMA Ophthalmology|September 4, 2020
Low-Shot Deep Learning of Diabetic Retinopathy With Potential Applications to Address Artificial Intelligence Bias in Retinal Diagnostics and Rare Ophthalmic DiseasesPhilippe Burlina, William Paul, Philip Mathew, et al.
JAMA Ophthalmology|January 11, 2019
Assessment of Deep Generative Models for High-Resolution Synthetic Retinal Image Generation of Age-Related Macular DegenerationPhilippe M Burlina, Neil Joshi, Katia D Pacheco, et al.
JAMA Ophthalmology|September 23, 2018
Use of Deep Learning for Detailed Severity Characterization and Estimation of 5-Year Risk Among Patients With Age-Related Macular DegenerationPhilippe M Burlina, Neil Joshi, Katia D Pacheco, et al.
JAMA Ophthalmology|October 4, 2017
Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural NetworksPhilippe M Burlina, Neil Joshi, Michael Pekala, et al.
Pageof 1