Sandra Morales

TL;DR: The algorithm proposed in this paper allows to automatically segment the optic disc from a fundus image to facilitate the early detection of certain pathologies and to fully automate the process so as to avoid specialist intervention.

TL;DR: Using ImageNet-trained models is a robust alternative for automatic glaucoma screening system and the high specificity and sensitivity obtained are supported by an extensive validation using not only the cross-validation strategy but also theCross-testing validation on, to the best of the authors’ knowledge, all publicly available glAUcoma-labelled databases.

TL;DR: A retinal image synthesizer and a semi-supervised learning method for automatic glaucoma assessment based on the deep convolutional GANs is trained, which is not only able to generate images synthetically but to provide labels automatically.

TL;DR: Results suggest that the method presented in this paper is a robust algorithm for describing retina texture and can be useful in a diagnosis aid system for retinal disease screening.

TL;DR: It is demonstrated that the proposed image segmentation method successfully detects the epidermal region in a fully automatic way in addition to defining the follicular skin structures as main novelty.