EGE-UNet

This is the official code repository for "EGE-UNet: an Efficient Group Enhanced UNet for skin lesion segmentation", which is accpeted by 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI2023) as a regular paper!

0. Main Environments

• python 3.8
• pytorch 1.8.0
• torchvision 0.9.0

1. Prepare the dataset.

• The ISIC17 and ISIC18 datasets, divided into a 7:3 ratio, can be found here {Baidu or GoogleDrive}.

• After downloading the datasets, you are supposed to put them into './data/isic17/' and './data/isic18/', and the file format reference is as follows. (take the ISIC17 dataset as an example.)

• './data/isic17/'

  • train
    • images
      • .png
    • masks
      • .png
  • val
    • images
      • .png
    • masks
      • .png

2. Train the EGE-UNet.

cd EGE-UNet

python train.py

3. Obtain the outputs.

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