| MIC-DKFZ/medicaldetectiontoolkit |
1,249 |
|
0 |
0 |
about 2 years ago |
0 |
|
44 |
apache-2.0 |
Python |
| 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. |
| TuSimple/TuSimple-DUC |
575 |
|
0 |
0 |
over 4 years ago |
0 |
|
6 |
apache-2.0 |
Python |
| Understanding Convolution for Semantic Segmentation |
| guosheng/refinenet |
500 |
|
0 |
0 |
almost 7 years ago |
0 |
|
0 |
other |
MATLAB |
| RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation |
| IBBM/Cascaded-FCN |
283 |
|
0 |
0 |
over 8 years ago |
0 |
|
7 |
other |
Jupyter Notebook |
| Source code for the MICCAI 2016 Paper "Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional NeuralNetworks and 3D Conditional Random Fields" |
| jeya-maria-jose/KiU-Net-pytorch |
245 |
|
0 |
0 |
almost 4 years ago |
0 |
|
8 |
mit |
Python |
| Official Pytorch Code of KiU-Net for Image/3D Segmentation - MICCAI 2020 (Oral), IEEE TMI |
| TUI-NICR/ESANet |
206 |
|
0 |
0 |
over 2 years ago |
0 |
|
32 |
other |
Python |
| ESANet: Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis |
| karolzak/keras-unet |
194 |
|
0 |
0 |
about 5 years ago |
7 |
July 27, 2020 |
8 |
mit |
Python |
| Helper package with multiple U-Net implementations in Keras as well as useful utility tools helpful when working with image semantic segmentation tasks. This library and underlying tools come from multiple projects I performed working on semantic segmentation tasks |
| drethage/fully-convolutional-point-network |
73 |
|
0 |
0 |
about 7 years ago |
0 |
|
2 |
mit |
Python |
| Fully-Convolutional Point Networks for Large-Scale Point Clouds |
| VisualComputingInstitute/3d-semantic-segmentation |
69 |
|
0 |
0 |
over 7 years ago |
0 |
|
5 |
mit |
Python |
| This work is based on our paper Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds, which is appeared at the IEEE International Conference on Computer Vision (ICCV) 2017, 3DRMS Workshop. |
| hjwdzh/TextureNet |
68 |
|
0 |
0 |
almost 7 years ago |
0 |
|
0 |
mit |
C++ |
| TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes |
