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javafx-sence-builder-test

javafx11-openjdk11-examples

javatenapp

joint-optic-disc-and-cup-segmentation-based-on-multi-label-deep-network-and-polar-transformation

Implementation of the paper -> Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation <- in Python3

jsf-eclipse-project

kaggle_diabetic_ram

Extended Retinopathy Detection Challenge with the Regression Activation Map for visual explaination

keras-cnn

A CNN for DR classification

keras-gan

Keras implementations of Generative Adversarial Networks.

kits19

The official repository of the 2019 Kidney and Kidney Tumor Segmentation Challenge

kittibox

A car detection model implemented in Tensorflow.

learning-history

learnopencv

Learn OpenCV : C++ and Python Examples

learnspringboot

mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

medical-image-classification-using-deep-learning

Tumour is formed in human body by abnormal cell multiplication in the tissue. Early detection of tumors and classifying them to Benign and malignant tumours is important in order to prevent its further growth. MRI (Magnetic Resonance Imaging) is a medical imaging technique used by radiologists to study and analyse medical images. Doing critical analysis manually can create unnecessary delay and also the accuracy for the same will be very less due to human errors. The main objective of this project is to apply machine learning techniques to make systems capable enough to perform such critical analysis faster with higher accuracy and efficiency levels. This research work is been done on te existing architecture of convolution neural network which can identify the tumour from MRI image. The Convolution Neural Network was implemented using Keras and TensorFlow, accelerated by NVIDIA Tesla K40 GPU. Using REMBRANDT as the dataset for implementation, the Classification accuracy accuired for AlexNet and ZFNet are 63.56% and 84.42% respectively.

medicaldetectiontoolkit

The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.

medicaltorch

A medical imaging framework for Pytorch

medicalzoopytorch

A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation

ml-lessons

Intro to deep learning for medical imaging lesson, by MD.ai

mnet_deepcdr

Code for TMI 2018 "Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation"

mockingbird

🚀AI拟声: 5秒内克隆您的声音并生成任意语音内容 Clone a voice in 5 seconds to generate arbitrary speech in real-time

modelzoo

A Scaffold to help you build Deep Learning Model much more easily, implemented with TensorFlow 2.0

multi-task-deep-network

Usage of Multi-task deep learning network for semantic segmentation in medical images

multi-task-modulation-module

Code release for paper "A Modulation Module for Multi-task Learning with Applications in Image Retrieval"

multibrain

A list of brain imaging datasets with multiple scans per subject. Feel free to update the list via 'pull requests'!

multiclass-diabetic-retinopathy-detection

This project is built to detect and determine Diabetic Retinopathy which affects more than half a million people around the world.

multiobjectiveoptimization

Source code for Neural Information Processing Systems (NeurIPS) 2018 paper "Multi-Task Learning as Multi-Objective Optimization"

named_entity_recognition

中文命名实体识别（包括多种模型：HMM，CRF，BiLSTM，BiLSTM+CRF的具体实现）

net2net.torch

Implementation of http://arxiv.org/abs/1511.05641 that lets one build a larger net starting from a smaller one.

neuralnetwork

numpy implementation of a feed forward neural network with backpropagation