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https://github.com/alexander-jing/transunet

This repository includes the official project of TransUNet, presented in our paper: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

https://github.com/alexander-jing/transunet

Science Score: 10.0%

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    CITATION.cff file
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    Links to: arxiv.org
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    Scientific vocabulary similarity
    Low similarity (9.8%) to scientific vocabulary
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Repository

This repository includes the official project of TransUNet, presented in our paper: TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

Basic Info
  • Host: GitHub
  • Owner: Alexander-Jing
  • License: apache-2.0
  • Default Branch: main
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Created over 4 years ago · Last pushed almost 5 years ago
Metadata Files
Readme License

README.md

TransUNet

This repo holds code for TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Usage

1. Download Google pre-trained ViT models

  • Get models in this link: R50-ViT-B16, ViT-B16, ViT-L16... ```bash wget https://storage.googleapis.com/vitmodels/imagenet21k/{MODELNAME}.npz && mkdir ../model/vitcheckpoint/imagenet21k && mv {MODELNAME}.npz ../model/vitcheckpoint/imagenet21k/{MODEL_NAME}.npz ```

2. Prepare data

Please go to "./datasets/README.md" for details, or please send an Email to jienengchen01 AT gmail.com to request the preprocessed data. If you would like to use the preprocessed data, please use it for research purposes and do not redistribute it.

3. Environment

Please prepare an environment with python=3.7, and then use the command "pip install -r requirements.txt" for the dependencies.

4. Train/Test

  • Run the train script on synapse dataset. The batch size can be reduced to 12 or 6 to save memory (please also decrease the base_lr linearly), and both can reach similar performance.

bash CUDA_VISIBLE_DEVICES=0 python train.py --dataset Synapse --vit_name R50-ViT-B_16

  • Run the test script on synapse dataset. It supports testing for both 2D images and 3D volumes.

bash python test.py --dataset Synapse --vit_name R50-ViT-B_16

Reference

Citations

bibtex @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} }

Owner

  • Name: Jing
  • Login: Alexander-Jing
  • Kind: user
  • Location: Beijing
  • Company: CASIA

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Dependencies

requirements.txt pypi
  • SimpleITK *
  • h5py *
  • medpy *
  • ml-collections *
  • numpy *
  • scipy *
  • tensorboard *
  • tensorboardX *
  • torch ==1.4.0
  • torchvision ==0.5.0
  • tqdm *