Transformer-based Models for Unsupervised Anomaly Segmentation in Brain MR Images

Official implementation of Transformer-based Models for Unsupervised Anomaly Segmentation in Brain MR Images.

Paper accepted in the International MICCAI Brainlesion 2022 Workshop

@InProceedings{10.1007/978-3-031-33842-7_3,
    author="Ghorbel, Ahmed
    and Aldahdooh, Ahmed
    and Albarqouni, Shadi
    and Hamidouche, Wassim",
    editor="Bakas, Spyridon
    and Crimi, Alessandro
    and Baid, Ujjwal
    and Malec, Sylwia
    and Pytlarz, Monika
    and Baheti, Bhakti
    and Zenk, Maximilian
    and Dorent, Reuben",
    title="Transformer Based Models for Unsupervised Anomaly Segmentation in Brain MR Images",
    booktitle="Brainlesion:  Glioma, Multiple Sclerosis, Stroke  and Traumatic Brain Injuries",
    year="2023",
    publisher="Springer Nature Switzerland",
    address="Cham",
    pages="25--44",
    isbn="978-3-031-33842-7"
}

• Transformers_Unsupervised_Anomaly_Segmentation
  • Requirements
  • Folder Structure
  • Usage
    • CLI-Usage
    • Google Colab Usage
  • Disclaimer
  • Reference
  • License

Tags

MICCAI BrainLes 2022 Workshop, Transformer, Autoencoder, TensorFlow, Keras, Anomaly Segmentation, Unsupervised, Neuroimaging, Deeplearning

Requirements

• Python >= 3.6

All packages used in this repository are listed in requirements.txt. To install those, run:

pip3 install -r requirements.txt

Folder Structure

  brain-anomaly-seg/
  ├── models/ - Models defining, training and evaluating
  │   ├── Autoencoders/
  │       ├── DCAE.py
  │       └── ...
  │   ├── Latent Variable models/
  │       ├── VAE.py
  │       └── ...
  │   └── Transformer based models/
  │       ├── B_TAE.py
  │       └── ...
  └── scripts/ - small utility scripts
      ├── utils.py
      └── ...    

Usage

CLI Usage

Every model can be trained and tested individually using the scripts which are provided in the models/* folders.

Disclaimer

Please do not hesitate to open an issue to inform of any problem you may find within this repository.

Reference

This project is inspired by the comparative study paper on Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study.

@article{baur2021autoencoders,
  title={Autoencoders for unsupervised anomaly segmentation in brain mr images: A comparative study},
  author={Baur, Christoph and Denner, Stefan and Wiestler, Benedikt and Navab, Nassir and Albarqouni, Shadi},
  journal={Medical Image Analysis},
  pages={101952},
  year={2021},
  publisher={Elsevier}
}

License

This project is licensed under the GNU General Public License v3.0. See LICENSE for more details