@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"
}

pip3 install -r requirements.txt

Transformers_Unsupervised_Anomaly_Segmentation/
│
├── Transformers_Unsupervised_Anomaly_Segmentation.ipynb - Jupyter notebook to work on Google Colab
│
├── data/
│   └── data.txt  - datasets descriptions and download link
│
├── models/ - Models defining, training and evaluating
│   ├── Autoencoders/
│       ├── DCAE.py
│       └── ...
│   ├── Latent Variable models/
│       ├── VAE.py
│       └── ...
│   └── Transformer based models/
│       ├── B_TAE.py
│       └── ...
│
├── saved/  - saving folder
│
└── scripts/ - small utility scripts
    ├── utils.py
    └── ...    

@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}
}