FedLSM

1. Download the ISIC dataset from ISIC 2018 Task3 and Chest-Xray14 NIH dataset from Chest-Xray14

2. Run the scprits (scripts/isic2018_prepare_fl.py and scripts/chestxray14_prepare_fl.py) to generate FL datasets, the corresponding configure file is in configs/cxr/scripts_conf.yaml and configs/isic/scripts_conf.yaml

3. Run the training code. python3 train.py --config "/home/project/FedLSM/configs/cxr/run_conf.yaml" python3 train.py --config "/home/project/FedLSM/configs/isic/run_conf.yaml"

4. Test the result python3 test.py --config "config file"
   --resume_path "model path"
   --test_csv_path "test csv file path"

citation

Please cite our paper if you find this code useful for your research.

@InProceedings{Deng2023, 
  author    = {Deng, Zhipeng and Luo, Luyang and Chen, Hao}, 
  title     = {Scale Federated Learning for Label Set Mismatch in Medical Image Classification},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  year      = {2023},
  pages= {118--127},
  organization={Springer}
}
'''