u-net-brain-tumor
Brain Tumor Segmentation Network
This repository demonstrates how to train a U-Net neural network for brain tumor segmentation using medical imaging data.
U-Net Brain Tumor Segmentation
507 stars
32 watching
180 forks
Language: Python
last commit: about 6 years ago
Linked from 2 awesome lists
medical-imagingtensorflowtensorlayerunet
Related projects:
| Repository | Description | Stars |
|---|---|---|
| | A convolutional neural network architecture for biomedical image segmentation | 430 |
| | A deep learning implementation of a multi-path network architecture for medical image segmentation | 140 |
| | An implementation of Spatial Transformer Networks in TensorFlow for learning to apply transformations to images via classification tasks. | 36 |
| | A PyTorch implementation of the U-Net architecture for brain MRI segmentation using pre-trained weights and a specific deep learning algorithm | 730 |
| | This project uses CNNs to segment breast cancer lesions from medical images. | 75 |
| | A PyTorch implementation of a deep learning model for semantic image segmentation | 1,598 |
| | An MXNet implementation of a modified deep neural network architecture for image classification | 67 |
| | Deep learning models for semantic segmentation of images | 101 |
| | An algorithmic framework to integrate gene expression data with spatial location and histological information to identify distinct regions in tissue samples. | 198 |
| | Re-implementation of a 100-layer fully convolutional network architecture for image segmentation | 123 |
| | An open-source implementation of an image segmentation model that combines background removal and object detection capabilities. | 1,484 |
| | Reimplementation of a neural network model for conditional segmentation of ambiguous images | 548 |
| | An implementation of a fully convolutional network architecture for image segmentation using VGG weights. | 1,102 |
| | Detects cancer metastasis in whole slide images using deep learning and conditional random fields | 757 |
| | Real-time semantic segmentation software for high-resolution images using a deep neural network architecture | 605 |