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awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

bcdu-net

BCDU-Net : Medical Image Segmentation

blood-vessels-segmentation-unet

Retinal blood vessel segmentation using convolutional neural network (u-net) and image processing techniques.

datasink

Heterogeneous ensemble learning.

deep-vessel

eye-fundus-image-segmentation

Segmentation vasculature in retinal image

heterogeneous_stacked_ensemble_classifier-super_learner

The Super Learner is a heterogeneous stacked ensemble classifier. This is a classification model that uses a set of base classifiers of different types, the outputs of which are then combined in another classifier at the stacked layer. The base classifiers are trained and their outputs are combined along with the training dataset labels into a training set for the stack layer classifier. To avoid overfitting the generation of the stacked layer training set uses a k-fold cross validation process. To further add variety to the base estimators a bootstrapping selection is used (as in the bagging ensemble approach).

patch2image

Training FCNNs from patches to full-sized images. A framework to train arbitrarily designed networks for medical image segmentation.

retina-blood-vessel-segmentation-in-pytorch

This repository contains the code for semantic segmentation of the retina blood vessel on the DRIVE dataset using the PyTorch framework.

retina-unet

Retina blood vessel segmentation with a convolutional neural network

retinal-blood-vessel-segmentation

Segmentation of retinal blood vessels using CNN and random forest machine learning techniques

retinal-vessel-segmentation

Retinal Vessel Segmentation based on Fully Convolutional Networks

riadd.aucmedi

Multi-Disease Detection in Retinal Imaging based on Ensembling Heterogeneous Deep Learning Models

s-metallograph-segmentation

Code for semantic segmentation models and techniques applied to metal microstructures, in collaboration with ArcelorMittal

semanticseg

Face Parsing [ hard ROI context ]

stacking

Stacked Generalization (Ensemble Learning)

stacking-blending-voting-ensembles

This repository contains an example of each of the Ensemble Learning methods: Stacking, Blending, and Voting. The examples for Stacking and Blending were made from scratch, the example for Voting was using the scikit-learn utility.

vessel-segmentation-in-retinal-fundus-images

👁️ Vessel segmentation in retinal fundus images

vesselseg-pytorch

Retinal vessel segmentation toolkit based on pytorch

vesselsegmentation_eswa

Support repository for the paper "Retinal vessel segmentation based on Fully Convolutional Neural Networks", Expert Systems with Applications, Volume 112, 1 December 2018, Pages 229-242.