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: πππOur 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])