machine learning

Learning normal asymmetry representations for homologous brain structures

The paper presents a novel method for learning normal asymmetry patterns in brain structures. It accurately characterizes normal asymmetries and detects pathological alterations without relying on diseased cases for training. The approach shows promise in improving the identification of neurodegenerative conditions..

NORHA: A NORmal Hippocampal Asymmetry deviation index based on one-class novelty detection and 3D shape features

NORHA is a novel index for quantifying hippocampal asymmetries in neurodegenerative conditions. It shows promise as a biomarker for detecting unilateral abnormalities, such as hippocampal sclerosis, and correlates positively with the functional cognitive test CDR-SB, indicating its potential in dementia diagnosis.

Postoperative vault prediction for phakic implantable collamer lens surgery: the LASSO formulae

We developed a new set of ICL sizing formulae based on LASSO regression, OCT measurements and ocular biometry data.

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.

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.

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.

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.

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.

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!

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.