0. Main Environments

• python 3.8
• pytorch 1.8.0
• torchvision 0.9.0

1. Prepare the dataset.

1- Download the ISIC 2017 train dataset from this link and extract both training dataset and ground truth folders inside the /data/dataset_isic17/.
2- Run Prepare_ISIC2017.py for data preparation and dividing data to train,validation and test sets.

Notice: For training and evaluating on ISIC 2018 and pH2 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 /data/dataset_isic18/.
then Run Prepare_ISIC2018.py for data preparation and dividing data to train,validation and test sets.
2- Download the ph2 dataset from this link and extract it then Run Prepare_PH2_test.py for data preperation and dividing data to train,validation and test sets.

2. Train the MHorUNet.

python train.py

• After trianing, you could obtain the outputs in './results/'

3. Test the MHorUNet. First, in the test.py file, you should change the address of the checkpoint in 'resume_model' and fill in the location of the test data in 'data_path'.

python test.py

• After testing, you could obtain the outputs in './results/'

Citation

If you find this repository helpful, please consider citing:

@article{wu2024mhorunet,
  title={MHorUNet: High-order spatial interaction UNet for skin lesion segmentation},
  author={Wu, Renkai and Liang, Pengchen and Huang, Xuan and Shi, Liu and Gu, Yuandong and Zhu, Haiqin and Chang, Qing},
  journal={Biomedical Signal Processing and Control},
  volume={88},
  pages={105517},
  year={2024},
  publisher={Elsevier}
}

References

• HorNet

• MALUNet