Mean-Teacher-Assisted Confident Learning (MTCL)

• Implementation of our work Mean-Teacher-assisted Confident Learning for learning segmentation from mixed-quality labeled data.
• Note that our label-denoising scheme aims at the binary task.

📚 Citation

If our work brings insights to you, or you use the codebase, please cite our papers as:

@article{xu2022anti,
  title={Anti-interference from Noisy Labels: Mean-Teacher-assisted Confident Learning for Medical Image Segmentation},
  author={Xu, Zhe and Lu, Donghuan and Luo, Jie and Wang, Yixin and Yan, Jiangpeng and Ma, Kai and Zheng, Yefeng and Tong, Raymond Kai-yu},
  journal={IEEE Transactions on Medical Imaging},
  year={2022},
  publisher={IEEE}
}

@artical{xu2021noisylabel,
  title={Noisy Labels are Treasure: Mean-Teacher-Assisted Confident Learning for Hepatic Vessel Segmentation},
  author={Zhe Xu, Donghuan Lu, Yixin Wang, Jie Luo, Jagadeesan Jayender, Kai Ma, Yefeng Zheng and Xiu Li},
  booktitle={International Conference on Medical Image Computing and Computer Assisted Intervention},
  year={2021}
}

🍻 Acknowledgement

The scripts are mainly based on the project SSL4MIS and the API cleanlab.