retinal imaging

We introduced a multitask learning y-shaped neural network that simultaneously segment the FAZ in FA images and predict a distance map. This extra branch aids to improve results in clinical routine images.

This is the summary publication of the REFUGE challenge on glaucoma detection in color fundus photographs.

We developed a Bayesian U-Net model for photoreceptor layer segmentation in OCT that predicts epistemic uncertainty maps highlighting potential areas of error in the segmentation.

We used CycleGANs to translate OCT images from one vendor to another. This approach allows us to increase the performance of fluid segmentation models trained on one vendor and evaluated on another.

We propose a deep learning methodology to predict retinal sensitivity from OCT volumes.

We used epistemic uncertainty estimates to discover potential abnormalities in diseased OCT scans. The uncertainty maps are obtained by a Bayesian U-Net trained on healthy OCT scans with weak labels of the retinal layers.

I will present part of my work of ophthalmic image analysis using machine/deep learning techniques as part of the closure event of the Deep Learning for Medical Image Analysis course that Enzo Ferrante is teaching at the University of Buenos Aires.

We designed a method to summarize hemodynamic parameters obtained by 0D simulations so that they can be applied for glaucoma detection. We observed certain correlation between glaucoma and these hemodynamic features.

We introduced a hybrid red lesion detection model based on a combination of deep learning based features and hand crafted descriptors.

We developed a simple linear regression model that is able to estimate the hyperparameters of a fully-connected CRF model for blood vessel segmentation in fundus images.