This repository includes the official project of TransUNet, presented in our paper: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.
APACHE-2.0 License
This repo holds code for TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
@article{chen2024transunet,
title={TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers},
author={Chen, Jieneng and Mei, Jieru and Li, Xianhang and Lu, Yongyi and Yu, Qihang and Wei, Qingyue and Luo, Xiangde and Xie, Yutong and Adeli, Ehsan and Wang, Yan and others},
journal={Medical Image Analysis},
pages={103280},
year={2024},
publisher={Elsevier}
}
wget https://storage.googleapis.com/vit_models/imagenet21k/{MODEL_NAME}.npz &&
mkdir ../model/vit_checkpoint/imagenet21k &&
mv {MODEL_NAME}.npz ../model/vit_checkpoint/imagenet21k/{MODEL_NAME}.npz
All data are available so no need to send emails for data. Please use the BTCV preprocessed data and ACDC data.
Please prepare an environment with python=3.7, and then use the command "pip install -r requirements.txt" for the dependencies.
CUDA_VISIBLE_DEVICES=0 python train.py --dataset Synapse --vit_name R50-ViT-B_16
python test.py --dataset Synapse --vit_name R50-ViT-B_16
@article{chen2021transunet,
title={TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation},
author={Chen, Jieneng and Lu, Yongyi and Yu, Qihang and Luo, Xiangde and Adeli, Ehsan and Wang, Yan and Lu, Le and Yuille, Alan L., and Zhou, Yuyin},
journal={arXiv preprint arXiv:2102.04306},
year={2021}
}