Breaking the Dilemma of Medical Image-to-image Translation

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.

6. 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}
}