Retinal Imaging

Machine learning for ophthalmic image analysis

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.

Dec 7, 2018 3:00 PM Pabellón I. Departamento de Computación, Facultad de Ciencias Exactas y Naturales. UBA.

José Ignacio Orlando

Machine learning for ophthalmic image analysis

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

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.

José Ignacio Orlando, Elena Prokofyeva, Mariana Del Fresno, Matthew B. Blaschko

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

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

I will present my SIPAIM 2017 paper on blood vessel segmentation in retinal images using FC-CRF with feature parameters estimated using linear regression.

Oct 6, 2017 3:00 PM Campus Universidad Nacional de Colombia

José Ignacio Orlando

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

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)

José Ignacio Orlando

Convolutional neural network transfer for automated glaucoma identification

We use pretrained VGG-S and OverFeat architectures as feature extractors for glaucoma detection in fundus pictures. We were able to get almost 0.8 AUC without fine-tuning the networks!

José Ignacio Orlando, Elena Prokofyeva, Mariana Del Fresno, Matthew B. Blaschko

Convolutional Neural Network Transfer for Automated Glaucoma Identification

I will present my SIPAIM 2016 paper on glaucoma classification using logistic regression and pre-trained neural networks as feature extractors.

Dec 6, 2016 11:15 AM Sala de Videoconferencias. Biblioteca Central. Campus Universitario Tandil.

José Ignacio Orlando

Convolutional Neural Network Transfer for Automated Glaucoma Identification

A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images

We present an extensive description and evaluation of our method for blood vessel segmentation in fundus images based on a discriminatively trained fully connected conditional random field model.

José Ignacio Orlando, Elena Prokofyeva, Matthew B Blaschko