/Reg-GAN

Primary LanguagePython

Breaking the Dilemma of Medical Image-to-image Translation

bat

Get the full paper on Arxiv. This paper has been accepted by NeurIPS 2021 (Spotlight).

Main Reference Environment

  1. Linux (Titan RTX)
  2. Python (3.6.6)
  3. torch (1.9.0+cu111)
  4. visdom (0.1.8.9)
  5. numpy (1.19.2)
  6. skimage (0.15.0)
  7. Yaml (5.4.1)
  8. cv2 (3.4.2)
  9. PIL (8.3.2)

Usage

  1. Create dataset
    • train path/A/
    • train path/B/
    • val path/A/
    • val path/B/
  2. The default data file form is .npy and normalized to [-1,1].
  3. Modify the parameters in the .yaml file as needed:
    • bidirect: whether to use bidirectional network, corresponding to the C or NC mode in the paper.
    • regist: whether the registration network is used, corresponding to the +R mode in the paper.
    • noise_level: set to 0 if you do not want to use noise.
    • port: port parameters of visdom.
  4. Default RegGAN mode (bidirect:False regist:True).
  5. Start visdom：
python -m visdom.server -p 6019

If other port parameters are used, you need to modify the port in yaml.

  1. Train:
python train.py

Trained Weights

We provide Pix2pix, CycleGAN, RegGAN trained weights under the condition of Noise.0: https://drive.google.com/file/d/1xWXB9u6dQ9ZytmgQl_0ph4H_Ivtd41zJ/view?usp=sharing

  • Pix2pix_noise0
  • CycleGAN_noise0
  • RegGAN_noise0

Processed data

We provide some processed data for your convenience： https://drive.google.com/file/d/1PiTzGQEVV7NO4nPaHeQv61WgDxoD76nL/view?usp=sharing

Citation

If you find RegGAN useful in your research, please consider citing:

@inproceedings{
kong2021breaking,
title={Breaking the Dilemma of Medical Image-to-image Translation},
author={Lingke Kong and Chenyu Lian and Detian Huang and ZhenJiang Li and Yanle Hu and Qichao Zhou},
booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
year={2021},
url={https://openreview.net/forum?id=C0GmZH2RnVR}
}