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100-go-mistakes

Source code and community space of 📖 100 Go Mistakes

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3dfasterrcnn_lungnoduledetector

6.869-final-project

An automatic glaucoma and diabetic retinopathy classification system

adaptive-bilateral-filter

asmonh

Active shape models for optic nerve head characterization in optical coherence tomography

awesome

😎 Awesome lists about all kinds of interesting topics

awesome-go-cn

Go 资源大全中文版， 内容包括：Web框架、模板引擎、表单、身份认证、数据库、ORM框架、图片处理、文本处理、自然语言处理、机器学习、日志、代码分析、教程和（电子）书等。由「开源前哨」和「Go开发大全」微信团队维护。

bgidb

a Browser Server software for the management of both fundus image and retinal OCT image

birdid_lasagne

Bird image classification using convolutional neural networks in Python

bisque_mirror

bloodvesselsegmentation

This is a code for blood vessel segmentation and tracking the blood vessels to measure the diameters. Only low-level image processing techniques are used.

blur-invariant-features

bmi212-glaucoma

boost_guide

This is the sample code for Boost library Guide.

browserbox

🌀 Browse the web from a browser you run on a server, rather than on your local device. Lightweight virtual browser. For security, privacy and more! By https://github.com/dosyago

bvr

Blood Vessel Reconstruction

caltech101classification

Caltech 101 dataset classification using neural networks and image preprocessing.

capsnet-tensorflow

A Tensorflow implementation of CapsNet(Capsules Net) in Hinton's paper Dynamic Routing Between Capsules

car-segmentation

kaggle car segmentation

catordogmachinelearningimageclassifierproject

Created and analyzed a machine learning project for determining if an image is a picture of a dog or of a cat. 1,170 images from the Kaggle data set were used for feature selection performed by a pre-trained Convolutional Neural Network to create a Cosine K-Nearest Neighbor classifier and a Cubic Support Vector Machine classifier. 25,000 images were then used for a second feature selection to create another Cubic Support Vector Machine since it was the better performing classifier. The entire machine learning workflow was performed including partitioning the data, feature selection, classifier selection, training, cross-validation and testing. The MATLAB Classification Learner App was used to help create the classifiers. This project was performed by two partners and myself for my Introduction to Artificial Intelligence elective course (CAP4630).

chanvese

Chan-Vese level-sets in Python

chap_2

Delineation of retinal layer through Deep learning and structured random forest

choroidsegmentation

Open-source algorithm for automatic choroid segmentation of OCT volume reconstructions. This software was developed by the HMR Biophotonics Lab, Hospital Maisonneuve-Rosemont, Montreal, Quebec, Canada (biophotonics.ca). It is open source and it was released under the GNU GENERAL PUBLIC LICENSE v3. It is only for research purposes. The algorithm is described in the paper "Open-source algorithm for automatic choroid segmentation of OCT volume reconstructions", Javier Mazzaferri, Luke Beaton, Gisèle Hounye, Diane N. Sayah, and Santiago Costantino, in press in Scientific Reports Jan 2017.

classification_of_sd_oct_using_lbp

Classification of SD-OCT using Local Binary Pattern and KNN Classifiers

cloud-classification

Cloud classification using CNN, KNN, SVM and MLP

cnn-cone-detection

convolutional-neural-network

CNN for image classification in Matlab

cpp11

CPP11新特新 代码实战

cxxnet

move forward to https://github.com/dmlc/mxnet