nitr098 / attswinunet

Official implementation code for Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation paper

AttSwinUNet

Official implementation code for Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation paper

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Prepare data and pretrained model

• [Get pre-trained model in this link] (https://drive.google.com/drive/folders/1UC3XOoezeum0uck4KBVGa8osahs6rKUY?usp=sharing): Put pretrained Swin-T into folder "pretrained_ckpt/" For training deep model and evaluating on each data set follow the bellow steps:
  1- Download the ISIC 2018 train dataset from this link and extract both training dataset and ground truth folders inside the dataset_isic18.
  2- Run Prepare_ISIC2018.py for data preperation and dividing data to train,validation and test sets.
  

Notice: For training and evaluating on ISIC 2017 and ph2 follow the bellow steps: :
ISIC 2017- Download the ISIC 2017 train dataset from this link and extract both training dataset and ground truth folders inside the dataset_isic18\7.
then Run Prepare_ISIC2017.py for data preperation and dividing data to train,validation and test sets.
ph2- Download the ph2 dataset from this link and extract it then Run Prepare_ph2.py for data preperation and dividing data to train,validation and test sets.

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Environment and Installation

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

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Train and Test

• The batch size we used is 24. If you do not have enough GPU memory, the bacth size can be reduced to 12 or 6 to save memory.

• Train

 python train.py --cfg configs/swin_tiny_patch4_window7_224_lite.yaml --root_path your DATA_DIR --max_epochs 150 --output_dir your OUT_DIR  --img_size 224 --base_lr 0.05 --batch_size 24 --mode cross_contextual_attention --spatial_attention 1

• Test

python test.py --cfg configs/swin_tiny_patch4_window7_224_lite.yaml --is_saveni --volume_path your DATA_DIR --output_dir your OUT_DIR --max_epoch 150 --base_lr 0.05 --img_size 224 --batch_size 24 --mode cross_contextual_attention --spatial_attention 1

• For Ablation study states, skip_num can be used to determine which skip connection the proposed module will be run on, which is 3 by default, that is, it will be run in all skip connections. To remove spatial attention, just set its flag to zero. Use swin mode to remove cross contextual attention module.

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Updates

• October 24, 2022: Submitted to ISBI2023 [Under Review].

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References

• Swin-Unet
• CrossViT

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Citation

@article{aghdam2022attention,
  title={Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation},
  author={Aghdam, Ehsan Khodapanah and Azad, Reza and Zarvani, Maral and Merhof, Dorit},
  journal={arXiv preprint arXiv:2210.16898},
  year={2022}
}