UniMiSS & UniMiSS+

This is the official pytorch implementation of our ECCV 2022 paper "UniMiSS: Universal Medical Self-Supervised Learning via Breaking Dimensionality Barrier" and extended IEEE-TPAMI paper "UniMiSS+: Universal Medical Self-Supervised Learning From Cross-Dimensional Unpaired Data".

To do

• [x] UniMiSS+ fine-tuning code
• [x] UniMiSS+ pre-training code and weights
• [x] UniMiSS fine-tuning code
• [x] UniMiSS pre-training code and weights

Citation

If this code is helpful for your study, please cite:

@article{UniMiSS,
  title={UniMiSS: Universal Medical Self-Supervised Learning via Breaking Dimensionality Barrier},
  author={Xie, Yutong and Zhang, Jianpeng and Xia, Yong and Wu, Qi},
  booktitle={ECCV},
  year={2022}
}
@article{UniMiSS+,
  title={UniMiSS+: Universal Medical Self-Supervised Learning From Cross-Dimensional Unpaired Data},
  author={Xie, Yutong and Zhang, Jianpeng and Xia, Yong and Wu, Qi},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2024},
  publisher={IEEE}
}

Acknowledgements

Part of codes is reused from the DINO. Thanks to Caron et al. for the codes of DINO.

Contact

Yutong Xie (yutong.xie678@gmail.com)