A New Framework of Swarm Learning Consolidating Knowledge from Multi-Center Non-IID Data for Medical Image Segmentation

This project is developed for the TMI paper: A New Framework of Swarm Learning Consolidating Knowledge from Multi-Center Non-IID Data for Medical Image Segmentation. In this paper, we proposed LaSA and FeSA loss to deal with the Non-IID challenge in distributed learning for partially supervised image segmentation. For more information, please read the following paper:

@ARTICLE{9943283,
  author={Gao, Zheyao and Wu, Fuping and Gao, Weiguo and Zhuang, Xiahai},
  journal={IEEE Transactions on Medical Imaging}, 
  title={A New Framework of Swarm Learning Consolidating Knowledge from Multi-Center Non-IID Data for Medical Image Segmentation}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TMI.2022.3220750}}

Environments

Pytorch == 1.8.1, cuda == 10.1, cudnn == 8.0

Usage

pip install -r requirements.txt
python main.py --mode nsl --weight 0.1 --loss UNCE

If you have any problems, please feel free to contact us. Thanks for your attention.