no code implementations • 20 Jul 2022 • Aaron Berk, Gulcenur Ozturan, Parsa Delavari, David Maberley, Özgür Yılmaz, Ipek Oruc
Here, we showcase results for the performance of DL on small datasets to classify patient sex from fundus images - a trait thought not to be present or quantifiable in fundus images until recently.
no code implementations • 25 Sep 2019 • Morgan Heisler, Forson Chan, Zaid Mammo, Chandrakumar Balaratnasingam, Pavle Prentasic, Gavin Docherty, MyeongJin Ju, Sanjeeva Rajapakse, Sieun Lee, Andrew Merkur, Andrew Kirker, David Albiani, David Maberley, K. Bailey Freund, Mirza Faisal Beg, Sven Loncaric, Marinko V. Sarunic, Eduardo V. Navajas
This study demonstrates the utility of deep neural networks for automatic quantification of foveal avascular zone (FAZ) parameters and perifoveal vessel density of OCT-A images in healthy and diabetic eyes.