📕 [arxiv]
2024/03/20
First release.
Please go to "Colab_BEFUnet.ipynb" for complete detail on dataset preparation and Train/Test procedure or follow the instructions below.
This code has been implemented in python language using Pytorch library and tested in ubuntu OS, though should be compatible with related environment. following Environement and Library needed to run the code: Python 3, Pytorch
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Run the following code to install the Requirements.
pip install -r requirements.txt
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Run the below code to train BEFUnet on the synapse dataset.
python train.py --root_path ./data/Synapse/train_npz --test_path ./data/Synapse/test_vol_h5 --model_name BEFUnet --batch_size 10 --eval_interval 20 --max_epochs 500
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Run the below code to test BEFUnet on the synapse dataset.
python test.py --test_path ./data/Synapse/test_vol_h5 --model_name BEFUnet --is_savenii --model_weight
We borrowed the code from Swin Transformer and PiDinet. Thanks for their wonderful works.
If you find this project helpful for your research, please consider citing the following BibTeX entry.
@article{manzari2024befunet,
title={BEFUnet: A Hybrid CNN-Transformer Architecture for Precise Medical Image Segmentation},
author={Manzari, Omid Nejati and Kaleybar, Javad Mirzapour and Saadat, Hooman and Maleki, Shahin},
journal={arXiv preprint arXiv:2402.08793},
year={2024}
}