Fundus Photography | José Ignacio Orlando

A ResNet is All You Need? Modeling A Strong Baseline for Detecting Referable Diabetic Retinopathy in Fundus Images

With no other methodological innovation than a carefully designed training, our ResNet model achieved an AUC = 0.955 (0.953 - 0.956) on a combined test set of 61007 test images from different public datasets, which is in line or even better than what other more complex deep learning models reported in the literature.

Tomás Castilla, Marcela S. Martínez, Mercedes Leguía, Ignacio Larrabide, José Ignacio Orlando

Assessing Coarse-to-Fine Deep Learning Models for Optic Disc and Cup Segmentation in Fundus Images

We experimentally validate whether using coarse-to-fine models instead of one-stage models is appropriate or not for segmenting the optic disc and the optic cup in color fundus images. We observed that models trained with the right amount of data can perform much better than coarse-to-find approaches.

Eugenia Moris, Nicolás Dazeo, María Paula Albina De Rueda, Francisco Filizzola, Nicolás Iannuzzo, Danila Nejamkin, Kevin Wignall, Mercedes Leguía, Ignacio Larrabide, José Ignacio Orlando

Assessing Coarse-to-Fine Deep Learning Models for Optic Disc and Cup Segmentation in Fundus Images

REFUGE challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs

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

José Ignacio Orlando, Huazhu Fu, João Barbossa Breda, Karel Van Keer, Deepti R. Bathula, Andrés Diaz-Pinto, Ruogu Fang, Pheng-Ann Heng, Jeyoung Kim, JoonHo Lee, Joonseok Lee, Xiaoxiao Li, Peng Liu, Shuai Lu, Balamurali Murugesan, Valery Naranjo, Sai Samarth R. Phaye, Sharath M. Shankaranarayana, Apoorva Sikka, Jaemin Son, Anton Van Den Hengel, Shujun Wang, Junyan Wu, Zifeng Wu, Guanghui Xu, Yongli Xu, Pengshuai Yin, Fei Li, Yanwu Xu, Xiulan Zhang, Hrvoje Bogunović

Towards a Glaucoma Risk Index Based on Simulated Hemodynamics from Fundus Images

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.

José Ignacio Orlando, João Barbosa Breda, Karel Van Keer, Matthew B. Blaschko, Pablo J. Blanco, Carlos A. Bulant

Retinal blood vessel segmentation in high resolution fundus photographs using automated feature parameter estimation

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.

José Ignacio Orlando, Marcos Fracchia, Valeria Del Río, Mariana Del Fresno

Proliferative diabetic retinopathy characterization based on fractal features: Evaluation on a publicly available dataset

We develop a fractal based model for detecting proliferative diabetic retinopathy cases from fundus pictures.

José Ignacio Orlando, Karel Van Keer, João Barbosa Breda, Hugo Luis Manterola, Matthew B Blaschko, Alejandro Clausse

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.

José Ignacio Orlando