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eess.IV2022★ 44 cited
A deep learning framework for the detection and quantification of drusen and reticular pseudodrusen on optical coherence tomography
Roy Schwartz, Hagar Khalid, Sandra Liakopoulos +12
Purpose - To develop and validate a deep learning (DL) framework for the detection and quantification of drusen and reticular pseudodrusen (RPD) on optical coherence tomography sca…
eess.IV2019★ 67 cited
A deep learning model for segmentation of geographic atrophy to study its long-term natural history
Bart Liefers, Johanna M. Colijn, Cristina González-Gonzalo +6
Purpose: To develop and validate a deep learning model for automatic segmentation of geographic atrophy (GA) in color fundus images (CFIs) and its application to study growth rate…