Deep Learning

Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid 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.

Linking Function and Structure: Prediction of Retinal Sensitivity in AMD from OCT using Deep Learning

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

Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT

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.

An ensemble deep learning based approach for red lesion detection in fundus images

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

Aprendizaje automático para asistencia al diagnóstico de enfermedades visuales basado en imágenes de fondo de ojo (Machine learning for ophthalmic screening and diagnostics from fundus images)

My PhD thesis.