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deepshape

deepshape-1

We use deep learning to predict RNA sture score. Design some image model and convert sequence to images to predict.

diabetic-retinopathy-deep-learning

To detect diabetic retinopathy from retinal images using deep learning

dicomread

read dicom file with opencv

dlcv_for_beginners

《深度学习与计算机视觉》配套代码

dnn_reg

Codes used for breast cancer margin assessment in OCT images using DNNs

elastalert-docker

elastalert docker镜像,开箱既用的集成了 微信企业号报警插件 和 钉钉报警插件(基于钉钉群机器人的webhook,支持签名安全认证，支持text和markdown格式)

forwardthinking

Forward Thinking NIPS 2017

fundus-vessel-segmentation

One of the first steps in automatic fundus image analysis is the segmentation of the retinal vasculature, which provides valuable information related to several diseases. 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. This task remains a challenge largely due to the desired structures being thin and elongated, a setting that performs particularly poorly using standard segmentation priors, such as a Potts model or total variation. 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. The evaluation of our method is performed both quantitatively and qualitatively on DRIVE, STARE, CHASEDB1 and HRF, showing its ability to deal with different types of images and outperforming other techniques, trained using state of the art features.

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.

fundus-vessel-segmentation-tmbe

One of the first steps in automatic fundus image analysis is the segmentation of the retinal vasculature, which provides valuable information related to several diseases. 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. This task remains a challenge largely due to the desired structures being thin and elongated, a setting that performs particularly poorly using standard segmentation priors, such as a Potts model or total variation. 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. The evaluation of our method is performed both quantitatively and qualitatively on DRIVE, STARE, CHASEDB1 and HRF, showing its ability to deal with different types of images and outperforming other techniques, trained using state of the art features.

gcforest

This is the official implementation for the paper 'Deep forest: Towards an alternative to deep neural networks'

gcforest-1

Python implementation of deep forest method : gcForest

glaucoma-project

glaucomadetector

Detecting Glaucoma using Self Organising Maps

go

【Go 从入门到实战】学习笔记，从零开始学 Go、Gin 框架，基本语法包括 26 个Demo，Gin 框架包括：Gin 自定义路由配置、Gin 使用 Logrus 进行日志记录、Gin 数据绑定和验证、Gin 自定义错误处理、Go gRPC Hello World... 持续更新中...

go-queue

go-queue-1

Kafka, Beanstalkd Pub/Sub framework.

go-stash

go-stash is a high performance, free and open source server-side data processing pipeline that ingests data from Kafka, processes it, and then sends it to ElasticSearch.

go-zero

A cloud-native Go microservices framework with cli tool for productivity.

golang-open-source-projects

为互联网IT人打造的中文版awesome-go

gorm

The fantastic ORM library for Golang, aims to be developer friendly (v2 is under public testing...)

harmonyos

A curated list of awesome things related to HarmonyOS. 华为鸿蒙操作系统。

have-fun-with-machine-learning

An absolute beginner's guide to Machine Learning and Image Classification with Neural Networks

image-classification-rnn

Classify MNIST image dataset into 10 classes. Build an image classifier with Recurrent Neural Network (RNN: LSTM) on Tensorflow.

image-classification-using-cnn

Classification of images in CIFAR 10 dataset uding a deep convolutional neural network architecture. The backpropagation formulae have been derived for the CNN and accordingly the algorithm developed. Mini batch gradient descent is used to learn the biases and filters of the network.

image-edge-detection

DCNN Based Image Edge Detection (FCN + HED + UNet + ResNet)

image-segmentation-tools

Ultimate goal: retinal layer segmentation of OCT images

image-segmentation-with-tensorflow

imageclassificationcnn