EGAN : Generative Adversarial Network for Skin Lesion Segmentations

Automatic lesion segmentation is a critical computer aided diagnosis (CAD) tool vital in ensuring effective treatment. Computer-aided diagnosis of such skin cancer on dermoscopic images can significantly reduce the clinicians’ workload and improve diagnostic accuracy. This code provides an adversarial learning-based segmentation framework that leverages the adversarial learning-based architecture (EGAN) for skin lesion segmentation. Specifically, this framework integrates two modules: The segmentation module and the discriminator module.

Getting Started

Install Requirements

tensorflow 2.x
keras=2.2.4
opencv
tqdm
scikit-image
segmentation_models from qubvel

Prerequisites

-GPU, CUDA

Running Evaluation

• Clone this repo:

git clone https://github.com/shubhaminnani/EGAN.git
cd EGAN

To reproduce the results for the rank in SKIN challenge in ISIC 2018, please do

python predict.py 0 # 0 is the avaliable GPU id, change is neccesary

Running Training for SKIN ISIC 2018 dataset

Remember to check/change the data path and weight path

python train_DGS.py 0
python test_DGS.py 0

Citation

@article{,
  journal={},
  title={EGAN},
  author={},
  year={},
  volume={},
  number={},
  pages={},
  publisher={},
  doi={},
  }