 Cascaded-FCN
 Cascaded-FCN 
 Image Segmentation Network
 An implementation of a Cascaded Fully Convolutional Neural Network architecture for medical image segmentation
Source code for the MICCAI 2016 Paper "Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional NeuralNetworks and 3D Conditional Random Fields"
304 stars
 38 watching
 127 forks
 
Language: Jupyter Notebook 
last commit: almost 8 years ago 
Linked from   1 awesome list  
  3d-crfcaffedeep-learningdeep-neural-networksfully-convolutional-networksliver-segmentationmedical-imagingsegmentationsemantic-segmentation 
 Related projects:
| Repository | Description | Stars | 
|---|---|---|
|  | A basic implementation of a fully convolutional network (FCN) for image segmentation in TensorFlow. | 9 | 
|  | A convolutional neural network architecture for biomedical image segmentation | 430 | 
|  | An implementation of a fully convolutional network architecture for image segmentation using VGG weights. | 1,102 | 
|  | Re-implementation of a 100-layer fully convolutional network architecture for image segmentation | 123 | 
|  | This code implements a neural network architecture designed to perform semantic segmentation in computer vision tasks. | 920 | 
|  | Deep learning models for semantic segmentation of images | 101 | 
|  | A Python implementation of a deep neural network architecture for semantic image segmentation | 48 | 
|  | An implementation of fully convolutional neural networks for semantic segmentation using TensorFlow as the backend. | 15 | 
|  | This project uses CNNs to segment breast cancer lesions from medical images. | 75 | 
|  | An implementation of a deep learning model for image segmentation using TensorFlow | 1,251 | 
|  | An implementation of fully convolutional networks in Chainer, a deep learning framework. | 218 | 
|  | A Matlab implementation of fully convolutional networks for image segmentation. | 175 | 
|  | An implementation of semantic segmentation using fully convolutional networks | 188 | 
|  | Develops a single convolutional network to handle various image degradations with improved scalability and efficiency | 427 | 
|  | A Python implementation of fully convolutional networks for semantic segmentation in computer vision. | 409 |