DC-UNet: Rethinking the U-Net Architecture with Dual Channel Efficient CNN for Medical Images Segmentation
🔥 NEWS 🔥 The pytorch version is available pytorch-version.
In this project, we test three datasets:
- Infrared Breast Dataset
- Endoscopy (CVC-ClinicDB)
- Electron Microscopy (ISBI-2012)
The following dependencies are needed:
- Kearas == 2.2.4
- Opencv == 3.3.1
- Tensorflow == 1.10.0
- Matplotlib == 3.1.3
- Numpy == 1.19.1
You can download the datasets you want to try, and just run:
main.py
If you think this work and code is helpful in your research, please cite:
@inproceedings{lou2021dc,
title={DC-UNet: rethinking the U-Net architecture with dual channel efficient CNN for medical image segmentation},
author={Lou, Ange and Guan, Shuyue and Loew, Murray H},
booktitle={Medical Imaging 2021: Image Processing},
volume={11596},
pages={115962T},
year={2021},
organization={International Society for Optics and Photonics}
}
@inproceedings{lou2019segmentation,
title={Segmentation of Infrared Breast Images Using MultiResUnet Neural Networks},
author={Lou, Ange and Guan, Shuyue and Kamona, Nada and Loew, Murray},
booktitle={2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)},
pages={1--6},
year={2019},
organization={IEEE}
}