44 citations · 44 across the 4 of their papers we have counts for
4 papers
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…
Unsupervised cross domain learning with applications to 7 layer segmentation of OCTs
Yue Wu, Abraham Olvera Barrios, Ryan Yanagihara +4
Unsupervised cross domain adaptation for OCT 7 layer segmentation and other medical applications where labeled training data is only available in a source domain and unavailable in…
Generalized Permutation Framework for Testing Model Variable Significance
Yue Wu, Ted Spaide, Kenji Nakamichi +2
A common problem in machine learning is determining if a variable significantly contributes to a model's prediction performance. This problem is aggravated for datasets, such as ge…
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
Objective: The advent of Electronic Medical Records (EMR) with large electronic imaging databases along with advances in deep neural networks with machine learning has provided a u…