This repository contains the official code of DS-TransUNet: Dual Swin Transformer U-Net for Medical Image Segmentation
Python==3.7.6
Pytorch==1.8.0
&&CUDA 11.1
timm==0.4.5
- Dataset
- Downloading training dataset and move it into
./data
, which can be found in this download link (Google Drive). - Downloading testing dataset and move it into
./data
, which can be found in this download link (Google Drive).
- Downloading training dataset and move it into
- Testing
- Downloading our trained DS-TransUNet-B from Baidu Pan (dd79), and move it into
./checkpoints
. - run
test_kvasir.py
- run
crcriteria.py
to get the DICE score, which uses EvaluateSegmentation. Or you can download our result images from Baidu Pan (dd79).
- Downloading our trained DS-TransUNet-B from Baidu Pan (dd79), and move it into
- Training
- downloading
Swin-T
andSwin-B
from Swin-Transformer to./checkpoints
. - run
train_kvasir.py
- downloading
Code of other tasks will be comming soon.
Some of the codes in this repo are borrowed from:
Please consider citing us if you find this work helpful:
@article{lin2022ds,
title={DS-TransUNet: Dual Swin Transformer U-Net for Medical Image Segmentation},
author={Lin, Ailiang and Chen, Bingzhi and Xu, Jiayu and Zhang, Zheng and Lu, Guangming and Zhang, David},
journal={IEEE Transactions on Instrumentation and Measurement},
year={2022},
publisher={IEEE}
}
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