Adversarial Data Augmentation with Chained Transformations (AdvChain)

This repo contains the pytorch implementation of adversarial data augmentation, which supports to perform adversarial training on a chain of image photometric transformations and geometric transformations for improved consistency regularization. Please cite our work if you find it useful in your work

Introduction

Under construction.

Requirements

• matplotlib>=2.0
• seaborn>=0.10.0
• numpy>=1.13.3
• SimpleITK>=2.1.0
• skimage>=0.0
• torch>=1.9.0

Set Up

1. Install PyTorch and other required python libraries with:

   pip install -r requirements.txt
   

2. Play with the provided jupyter notebook to check the enviroments

Usage

Under construction

Citation

If you find this useful for your work, please consider citing

@INPROCEEDINGS{Chen_MICCAI_2020_Realistic,
  title     = "Realistic Adversarial Data Augmentation for {MR} Image
               Segmentation",
  booktitle = "Medical Image Computing and Computer Assisted Intervention --
               {MICCAI} 2020",
  author    = "Chen, Chen and Qin, Chen and Qiu, Huaqi and Ouyang, Cheng and
               Wang, Shuo and Chen, Liang and Tarroni, Giacomo and Bai, Wenjia
               and Rueckert, Daniel",
  publisher = "Springer International Publishing",
  pages     = "667--677",
  year      =  2020
}