HSH-UNet: Hybrid selective high order interactive U-shaped model for automated skin
lesion segmentation
[paper link]
Renkai Wu, Hongli Lv, Pengchen Liang, Xiaoxu Cui, Qing Chang*, Xuan Huang*,
0. Main Environments
- python 3.8
- pytorch 1.12.0
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 preparation and dividing data to train,validation and test sets.
2. Train the HSH-UNet.
python train.py
- After trianing, you could obtain the outputs in './results/'
3. Test the HSH-UNet. 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/'