1 citations · 1 across the 2 of their papers we have counts for
3 papers
eess.IV2021★ 1 cited
Resource and data efficient self supervised learning
Ozan Ciga, Tony Xu, Anne L. Martel
We investigate the utility of pretraining by contrastive self supervised learning on both natural-scene and medical imaging datasets when the unlabeled dataset size is small, or wh…
eess.IV2020
Overcoming the limitations of patch-based learning to detect cancer in whole slide images
Ozan Ciga, Tony Xu, Sharon Nofech-Mozes +3
Whole slide images (WSIs) pose unique challenges when training deep learning models. They are very large which makes it necessary to break each image down into smaller patches for…
eess.IV2020
Self supervised contrastive learning for digital histopathology
Ozan Ciga, Tony Xu, Anne L. Martel
Unsupervised learning has been a long-standing goal of machine learning and is especially important for medical image analysis, where the learning can compensate for the scarcity o…