MHorUNet:High-order Spatial Interaction UNet for Skin Lesion Segmentation [paper link]
Renkai Wu, Pengchen Liang, Xuan Huang, Liu Shi, Yuandong Gu, Haiqin Zhu*, Qing Chang*
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/'
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}
}