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".

• 5 Aug 2024: 🎉🎉🎉Our extended version UniMiSS+ has been accepted by IEEE-TPAMI (Impact Factor=20.8). Code will be released soon.

• UniMiSS+ fine-tuning code
• UniMiSS+ pre-training code and weights
• UniMiSS fine-tuning code
• UniMiSS pre-training code and weights

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}
}

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

Yutong Xie ([email protected])