[MICCAI2021] This is an official PyTorch implementation for "Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation"
This repo contains the supported pytorch code and configuration files to reproduce medical image segmentaion results of Duo-SegNet.
and denote Segmentation networks and Critic network. Here, Critic criticizes between prediction masks and the ground truth masks to perform the min-max game.
Please prepare an environment with python=3.8, and then run the command "pip install -r requirements.txt" for the dependencies.
For experiments we used three datasets:
File structure
data
├── nuclei
| ├── train
│ │ ├── image
│ │ │ └── 00ae65...
│ │ └── mask
│ │ └── 00ae65...
├── spleen
├── heart
│
|
Duo-SegNet
├──train.py
...
Use Med2Image to convert NIFTI to PNG.
python train.py --dataset nuclei --ratio 0.05 --epoch 200
python test.py --dataset nuclei
This repository makes liberal use of code from Deep Co-training and pytorch-CycleGAN-and-pix2pix
@inproceedings{peiris2021duo,
title={Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation},
author={Peiris, Himashi and Chen, Zhaolin and Egan, Gary and Harandi, Mehrtash},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={428--438},
year={2021},
organization={Springer}
}