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.