FPL-UDA

The codes for the paper FPL-UDA:FILTERED PSEUDO LABEL-BASED UNSUPERVISED CROSS-MODALITY ADAPTATION FOR VESTIBULAR SCHWANNOMA SEGMENTATION

At the same time, this method has achieved good results in CrossMoDA 2021 Challenge (https://arxiv.org/pdf/2201.02831.pdf).

Part 1: GAN-Based Data Augmentation for G

1. Train the CycleGAN

2. Train the CUT

3. Train the pseudo label generator

sh train_pseudo_label_generator.sh

Part 2: Pseudo Label-Assisted Two-Stage Translation

Part 3: Uncertainty-Based Filtering of Pseudo Labels for S

sh get_normalized_uncertainty.sh

sh train_final_segmentor.sh

Citation

If you have any questions, please send us email [email protected].

If you find our work is useful, please cite our work.

@inproceedings{wu2022fpl,
  title={FPL-UDA: Filtered Pseudo Label-Based Unsupervised Cross-Modality Adaptation for Vestibular Schwannoma Segmentation},
  author={Wu, Jianghao and Gu, Ran and Dong, Guiming and Wang, Guotai and Zhang, Shaoting},
  booktitle={2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)},
  pages={1--5},
  year={2022},
  organization={IEEE}
}
@article{dorent2022crossmoda,
  title={CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwnannoma and Cochlea Segmentation},
  author={Dorent, Reuben and Kujawa, Aaron and Ivory, Marina and Bakas, Spyridon and Rieke, Nicola and Joutard, Samuel and Glocker, Ben and Cardoso, Jorge and Modat, Marc and Batmanghelich, Kayhan and others},
  journal={arXiv preprint arXiv:2201.02831},
  year={2022}
}