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PEFAT: Boosting Semi-supervised Medical Image Classification via Pseudo-loss Estimation and Feature Adversarial Training

Introduction

This repository is for CVPR2023 paper 'PEFAT: Boosting Semi-supervised Medical Image Classification via Pseudo-loss Estimation and Feature Adversarial Training'.

Usage

Train the model

python ./code/train.py

Citation

If this repository is useful for your research, please consider citing:

@inproceedings{zeng2023pefat,
  title={PEFAT: Boosting Semi-Supervised Medical Image Classification via Pseudo-Loss Estimation and Feature Adversarial Training},
  author={Zeng, Qingjie and Xie, Yutong and Lu, Zilin and Xia, Yong},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={15671--15680},
  year={2023}
}

Acknowledgement

This work is mainly based on SRC-MT, VAT, M-DYR and DivideMix. Thanks for these authors for their valuable works.