https://github.com/nibtehaz/MultiResUNet
MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation
https://github.com/nibtehaz/MultiResUNet
medical-imaging segmentation-models
Last synced: 4 months ago
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MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation
- Host: GitHub
- URL: https://github.com/nibtehaz/MultiResUNet
- Owner: nibtehaz
- License: mit
- Created: 2019-02-04T18:12:31.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2022-10-24T15:32:06.000Z (over 3 years ago)
- Last Synced: 2023-10-25T18:34:53.694Z (over 2 years ago)
- Topics: medical-imaging, segmentation-models
- Language: Jupyter Notebook
- Homepage:
- Size: 439 KB
- Stars: 370
- Watchers: 11
- Forks: 83
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-unet-instance-segmentation- - Neural Networks 2020
README
# MultiResUNet
#### Rethinking the U-Net architecture for multimodal biomedical image segmentation
This repository contains the original implementation of "MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation" in Keras (Tensorflow as backend).
## Paper
MultiResUNet has been published in Neural Networks
>Ibtehaz, Nabil, and M. Sohel Rahman. "MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation." Neural Networks 121 (2020): 74-87.
* [Read the Paper](https://doi.org/10.1016/j.neunet.2019.08.025)
* [View the Preprint](https://arxiv.org/abs/1902.04049)
## Overview
In this project we take motivations from the phenomenal U-Net architecture for biomedical image segmentation and take an attempt to improve the already outstanding network.
In order to incorporate multiresolution analysis, taking inspiration from Inception family networks, we propose the following *MultiRes* block, and replace the pair of convolutional layer pairs in the original U-Net with it. This configuration basically is derived from factorizing 5x5 and 7x7 convolution operations to 3x3 ones, and reusing them to obtain results from 3x3, 5x5 and 7x7 convolution operations simultaneously. Moreover, a residual path is also added.
Consequnetly, to elleviate the likely semantic distance between Encoder and Decoder networks, we introduce *Res* Paths. We include additional convolutions along the shortcut path, in proportionate to the expected gap between the two corresponding layers.
Therefore, with the fusion of *MultiRes* blocks and *Res* paths, we obtain the proposed architecture ***MultiResUNet***.
## Codes
The model architecture codes can be found in
### Tensorflow
* [2D Model](https://github.com/nibtehaz/MultiResUNet/blob/master/tensorflow/MultiResUNet.py)
* [3D Model](https://github.com/nibtehaz/MultiResUNet/blob/master/tensorflow/MultiResUNet3D.py)
### Pytorch
* [2D Model](https://github.com/nibtehaz/MultiResUNet/blob/master/pytorch/MultiResUNet.py)
* [3D Model](https://github.com/nibtehaz/MultiResUNet/blob/master/pytorch/MultiResUNet3D.py)
## Demo
A demo can be found in [here](https://github.com/nibtehaz/MultiResUNet/blob/master/tensorflow/Demo.ipynb) (tensorflow)
## License
[](http://badges.mit-license.org)
[MIT license](https://github.com/nibtehaz/MultiResUNet/blob/master/LICENSE)
## Citation Request
If you use ***MultiResUNet*** in your project, please cite the following paper
```
@article{ibtehaz2020multiresunet,
title={MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation},
author={Ibtehaz, Nabil and Rahman, M Sohel},
journal={Neural Networks},
volume={121},
pages={74--87},
year={2020},
publisher={Elsevier}
}
```