1 citations · 1 across the 3 of their papers we have counts for
3 papers
Semi-Supervised Multi-Task Learning for Interpretable Quality As- sessment of Fundus Images
Lucas Gabriel Telesco, Danila Nejamkin, Estefanía Mata +8
Retinal image quality assessment (RIQA) supports computer-aided diagnosis of eye diseases. However, most tools classify only overall image quality, without indicating acquisition d…
A ResNet is All You Need? Modeling A Strong Baseline for Detecting Referable Diabetic Retinopathy in Fundus Images
Tomás Castilla, Marcela S. Martínez, Mercedes Leguía +2
Deep learning is currently the state-of-the-art for automated detection of referable diabetic retinopathy (DR) from color fundus photographs (CFP). While the general interest is pu…
Assessing Coarse-to-Fine Deep Learning Models for Optic Disc and Cup Segmentation in Fundus Images
Eugenia Moris, Nicolás Dazeo, Maria Paula Albina de Rueda +7
Automated optic disc (OD) and optic cup (OC) segmentation in fundus images is relevant to efficiently measure the vertical cup-to-disc ratio (vCDR), a biomarker commonly used in op…