This repository contains an implementation of the following paper: :point_down:
V. K. Singh et al., "FCA-Net: Adversarial Learning for Skin Lesion Segmentation Based on Multi-Scale Features and Factorized Channel Attention," in IEEE Access, vol. 7, pp. 130552-130565, 2019, doi: 10.1109/ACCESS.2019.2940418.
- Linux
- Python with numpy
- NVIDIA GPU + CUDA 8.0 + CuDNNv5.1
- pytorch 4.0/4.1
- torchvision
+ Clone this repo:
cd Skin-Project
+ Get dataset
unzip dataset/skin.zip
+ Train the model:
python train.py --dataset skin --nEpochs 200 --cuda
+ Test the model:
python test.py --dataset skin --model checkpoint/skin/netG_model_epoch_100.pth --cuda
If you use the code in your work, please use the following citation:
@ARTICLE{8832175,
author={V. K. {Singh} and M. {Abdel-Nasser} and H. A. {Rashwan} and F. {Akram} and N. {Pandey} and A. {Lalande} and B. {Presles} and S. {Romani} and D. {Puig}},
journal={IEEE Access},
title={FCA-Net: Adversarial Learning for Skin Lesion Segmentation Based on Multi-Scale Features and Factorized Channel Attention},
year={2019},
volume={7},
number={},
pages={130552-130565},
doi={10.1109/ACCESS.2019.2940418}}