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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

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/'