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Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation

Introduction

This repository contains the PyTorch implementation of:

Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation, MICCAI 2021.

Requirements

• torch
• torchvision
• tqdm
• opencv
• scipy
• skimage
• PIL
• numpy

Usage

1. Training

python main.py  --mode train  --manner semi --ratio 2 
--root {project_path} --dataset polyp --polyp {data_path}

2. Inference

python main.py  --mode test  --manner test --load_ckpt checkpoint 
--root {project_path} --dataset polyp --polyp {data_path}

Citation

If you feel this work is helpful, please cite our paper

@inproceedings{zhang2021self,
  title={Self-supervised Correction Learning for Semi-supervised Biomedical Image Segmentation},
  author={Zhang, Ruifei and Liu, Sishuo and Yu, Yizhou and Li, Guanbin},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={134--144},
  year={2021},
  organization={Springer}
}