DAEFormer

The official code for "DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image Segmentation".

Updates

29 Dec., 2022: Initial release with arXiv.
27 Dec., 2022: Submitted to MIDL 2023 [Under Review].

Citation

@article{azad2022daeformer,
  title={DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image Segmentation},
  author={Azad, Reza and Arimond, René and Aghdam, Ehsan Khodapanah and Kazerouni, Amirhosein and Merhof, Dorit},
  journal={arXiv preprint arXiv:2212.13504},
  year={2022}
}

How to use

The script train.py contains all the necessary steps for training the network. A list and dataloader for the Synapse dataset are also included. To load a network, use the --module argument when running the train script (--module <directory>.<module_name>.<class_name>, e.g. --module networks.DAEFormer.DAEFormer)

Model weights

You can download the learned weights of the DAEFormer in the following table.

Task	Dataset	Learned weights
Multi organ segmentation	Synapse	DAEFormer

Training and Testing

Download the Synapse dataset from here.
Run the following code to install the Requirements.

pip install -r requirements.txt
Run the below code to train the DAEFormer on the synapse dataset.
```
python train.py --root_path ./data/Synapse/train_npz --test_path ./data/Synapse/test_vol_h5 --batch_size 20 --eval_interval 20 --max_epochs 400 --module networks.DAEFormer.DAEFormer
```
--root_path [Train data path]

--test_path [Test data path]

--eval_interval [Evaluation epoch]

--module [Module name, including path (can also train your own models)]
Run the below code to test the DAEFormer on the synapse dataset.
```
python test.py --volume_path ./data/Synapse/ --test_path ./data/Synapse/test_vol_h5 --output_dir './model_out'
```
--volume_path [Root dir of the test data]

--test_path [Test data path]

--output_dir [Directory of your learned weights]

Results

Performance comparision on Synapse Multi-Organ Segmentation dataset.

Query

All implementation done by Rene Arimond. For any query please contact us for more information.

rene.arimond@lfb.rwth-aachen.de

seanreed1111/DAEFormer