Machine Learning | José Ignacio Orlando

Machine learning for filtering out false positive grey matter atrophies in single subject voxel based morphometry: A simulation based study

We used SVMs to filter out false positive detections causes by single subject VBM when applied for discovering abnormalities in MRI scans.

Hernán C. Külsgaard, José Ignacio Orlando, Mariana Bendersky, Juan P. Princich, Luis S. R. Manzanera, Alberto Vargas, Silvia Kochen, Ignacio Larrabide

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

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

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

Assessment of image features for vessel wall segmentation in intravascular ultrasound images

We explored and evaluated different feature extraction techniques in the context of IVUS segmentation.

Lucas Lo Vercio, José Ignacio Orlando, Mariana Del Fresno, Ignacio Larrabide

Learning Fully-Connected CRFs for Blood Vessel Segmentation in Retinal Images

We introduced a discriminatively trained fully-connected conditional random field model for blood vessel segmentation in retinal images.

José Ignacio Orlando, Matthew B. Blaschko

Arabidopsis Roots Segmentation Based on Morphological Operations and CRFs

We introduced a model for Arabidopsis thaliana root segmentation based on CRFs.

José Ignacio Orlando, Hugo Luis Manterola, Enzo Ferrante, Federico Ariel