Assessing glaucoma in retinal fundus photographs using Deep Feature Consistent Variational Autoencoders

no code implementations • 4 Oct 2021 • Sayan Mandal, Alessandro A. Jammal, Felipe A. Medeiros

One of the leading causes of blindness is glaucoma, which is challenging to detect since it remains asymptomatic until the symptoms are severe.

RetiNerveNet: Using Recursive Deep Learning to Estimate Pointwise 24-2 Visual Field Data based on Retinal Structure

no code implementations • 15 Oct 2020 • Shounak Datta, Eduardo B. Mariottoni, David Dov, Alessandro A. Jammal, Lawrence Carin, Felipe A. Medeiros

Due to the SAP test's innate difficulty and its high test-retest variability, we propose the RetiNerveNet, a deep convolutional recursive neural network for obtaining estimates of the SAP visual field.

From Machine to Machine: An OCT-trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs

no code implementations • 20 Oct 2018 • Felipe A. Medeiros, Alessandro A. Jammal, Atalie C. Thompson

The mean prediction of average RNFL thickness from all 6, 292 optic disc photos in the test set was 83. 3$\pm$14. 5 $\mu$m, whereas the mean average RNFL thickness from all corresponding SDOCT scans was 82. 5$\pm$16. 8 $\mu$m (P = 0. 164).