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auc-ci-bootstrap

Python Implementation from computing AUC, Se and Sp with confidence intervals through bootstrapping

cnn-dr-kaggle

Diabetic retinopathy detection using Convolutional Neural Networks. This code is part of our project with Pablo Rubí, Nicolás Dazeo, Carlos Bulant and Hugo Luis Manterola.

cnn-finetune

Fine-tuning CNNs with MatConvNet

deep-roots

dr-cnn

Convolutional neural networks for diabetic retinopathy detection

duia-cv-datasets

Repositorio con links a bases de datos útiles para probar y entrenar algoritmos de visión computacional basados en inteligencia artificial.

duia-ml-datasets

fdim

FDim is a GUI to compute the fractal dimension of a grayscale image. It supports the capacity, information, correlation, and probability dimension algorithms.

fundus-fractal-analysis

This code corresponds to our Medical Physics paper with Karel van Keer, João Barbosa Breda, Hugo Luis Manterola, Matthew B. Blaschko and Alejandro Clausse, entitled "Proliferative Diabetic Retinopathy Characterization based on Fractal Features: Evaluation on a Publicly Available Data Set".

fundus-util

fundus-vessel-segmentation-tbme

In this work, 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. Standard segmentation priors such as a Potts model or total variation usually fail when dealing with thin and elongated structures. We overcome this difficulty by using a conditional random field model with more expressive potentials, taking advantage of recent results enabling inference of fully connected models almost in real-time. Parameters of the method are learned automatically using a structured output support vector machine, a supervised technique widely used for structured prediction in a number of machine learning applications. Our method, trained with state of the art features, is evaluated both quantitatively and qualitatively on four publicly available data sets: DRIVE, STARE, CHASEDB1 and HRF. Additionally, a quantitative comparison with respect to other strategies is included. The experimental results show that this approach outperforms other techniques when evaluated in terms of sensitivity, F1-score, G-mean and Matthews correlation coefficient. Additionally, it was observed that the fully connected model is able to better distinguish the desired structures than the local neighborhood based approach. Results suggest that this method is suitable for the task of segmenting elongated structures, a feature that can be exploited to contribute with other medical and biological applications.

glaucoma-hemodynamics

This code corresponds to our MICCAI 2018 paper on retinal hemodynamics simulation. If you use this code, please cite: Orlando, JI, Barbosa Breda, J, van Keer, K, Blaschko, MB, Blanco, PJ and Bulant, C. "Towards a glaucoma risk index based on simulated hemodynamics from fundus images". MICCAI 2018.

hackaton-agrodatos

deepcamera comprises our code with @blito on deep learning and entropy analysis for motion estimation in fishing surveillance video sequences. We made this as part of Hackaton Agro 2016 (Tandil, Argentina).

high-resolution-vessel-segmentation

This repository contains the blood vessel segmentation masks obtained using our method for blood vessel segmentation based on automated feature parameter estimation.

ignaciorlando.github.io

keras-tutorial-cifar10

matconvnet

MatConvNet: CNNs for MATLAB

matconvnet-fcn

A MatConvNet-based implementation of the Fully-Convolutional Networks for image segmentation

modelizacion-exactas

Este repositorio corresponde a nuestro trabajo de cursada/final de la materia Modelización, que se dicta como parte de la Licenciatura en Matemática de la Facultad de Ciencias Exactas de la UNCPBA. La materia también es válida como curso de postgrado para el Doctorado en Matemática Computacional e Industrial de la misma facultad. El trabajo consiste en optimizar mediante cutting-planes el recorrido más corto en un grafo con cotas de tiempo.

overfeat-glaucoma

This code corresponds to our paper with Matthew B. Blaschko, Elena Prokofyeva and Mariana del Fresno on Convolutional neural network transfer for automated glaucoma identification (SIPAIM 2016).

photoreceptor-segmentation

This code corresponds to the implementation of the U-shaped deep neural networks that we used in our Scientific Reports paper on photoreceptor segmentation in OCT scans

pix2pix

Image-to-image translation using conditional adversarial nets

plant-root-analysis

Arabidopsis thaliana is a plant species widely utilized by scientists to estimate the impact of genetic differences in root morphological features. For this purpose, images of this plant after genetic modifications are taken to study differences in the root architecture. This task requires manual segmentations of radicular structures, although this is a particularly tedious and time-consuming labor. In this work, we present an unsupervised method for Arabidopsis thaliana root segmentation based on morphological operations and fully-connected Conditional Random Fields. Although other approaches have been proposed to this purpose, all of them are based on more complex and expensive imaging modalities. Our results prove that our method can be easily applied over images taken using conventional scanners, with a minor user intervention. A first data set, our results and a fully open source implementation are available online.

red-lesion-detection

This code implements a red lesion detection method based on a combination of hand-crafted features and CNN based descriptors. Our paper is under revision now, so please do not use this repository until we release the paper.

refuge-evaluation

This repository corresponds to the evaluation code used for the REFUGE challenge. Please, use it as a sanity check to verify that the format of your submissions are correct. The formatting instructions are provided in the website.

resnet-matconvnet

Train Deep Residual Network from Scratch or or Fine-tune Pre-trained Model using Matconvnet

retina-unet

Retina blood vessel segmentation with a convolutional neural network

retinal-hemodynamics

Code for simulating the retinal hemodynamics from vessel segmentations extracted from fundus images.

root-ar

This is the official repository of our Arabidopsis thaliana root segmentation method. Use of this code is strictly forbidden yet. We will publicly release it for research purposes once our paper is accepted :)

svm-struct-matlab

A MATLAB wrapper of SVM^struct