FedISCA

MICCAI2023. "One-shot Federated Learning on Medical Data using Knowledge Distillation with Image Synthesis and Client Model Adaptation"

Train Classifiers

# bloodmnist, dermamnist, octmnist, pathmnist, tissuemnist
CUDA_VISIBLE_DEVICES=0 python train_classifier.py \
    --root ./dataset \
    --output_dir ./pretrained_models \
    --dataset bloodmnist --partition iid

# rsna
CUDA_VISIBLE_DEVICES=0 python train_classifier.py  \
    --root ./dataset \
    --output_dir ./pretrained_models \
    --dataset rsna --aug --pretrained --partition iid

# diabetic2015
CUDA_VISIBLE_DEVICES=0 python train_classifier.py  \
    --root ./dataset \
    --output_dir ./pretrained_models \
    --dataset diabetic2015 --aug --pretrained --partition iid

# isic2019_merge
CUDA_VISIBLE_DEVICES=0 python train_classifier.py  \
    --root ./dataset \
    --output_dir ./pretrained_models \
    --dataset isic2019_merge --aug --pretrained --partition iid

# isic2019
CUDA_VISIBLE_DEVICES=0 python train_classifier.py  \
    --root ./dataset \
    --output_dir ./pretrained_models \
    --dataset isic2019 --aug --pretrained

Run FedISCA

python
import os
GPU='0'
dataset_tag_list = ['iid_5_0.6']
dataset_list = ['bloodmnist']  # 'bloodmnist', 'dermamnist', 'octmnist', 'pathmnist', 'tissuemnist'
for dataset_tag in dataset_tag_list:
    for dataset in dataset_list:
        os.system(f"CUDA_VISIBLE_DEVICES={GPU} python main_fedisca.py \
            --dataset {dataset} \
            --root ./dataset \
            --teacher_weights=./pretrained_models/{dataset}_{dataset_tag} \
            --exp_descr=./results/oneshot_{dataset_tag}/{dataset}")

python
import os
GPU='0'
dataset_list = ['rsna']  # 'rsna', 'diabetic2015'
dataset_tag_list = ['iid_5_0.6']
for dataset_tag in dataset_tag_list:
    for dataset in dataset_list:
        os.system(f"CUDA_VISIBLE_DEVICES={GPU} python main_fedisca.py \
            --dataset {dataset} \
            --root ./dataset \
            --teacher_weights=./pretrained_models/{dataset}_{dataset_tag} --pretrained \
            --exp_descr=./results/oneshot_{dataset_tag}/{dataset}_pretrained --bs 50 --iters_mi 1000")

python
import os
GPU='0'
dataset_list = ['isic2019']
dataset_tag_list = ['merge_iid_5_0.6']  # 'merge_iid_5_0.6', 'dirichlet_6_0.6'
for dataset_tag in dataset_tag_list:
    for dataset in dataset_list:
        os.system(f"CUDA_VISIBLE_DEVICES={GPU} python main_fedisca.py \
            --dataset {dataset} \
            --root ./dataset \
            --teacher_weights=./pretrained_models/{dataset}_{dataset_tag} --pretrained \
            --exp_descr=./results/oneshot_{dataset_tag}/{dataset}_pretrained --bs 50 --iters_mi 1000")

Datasets

# small-scale datasets
https://medmnist.com/
# download bloodmnist, dermamnist, octmnist, pathmnist, tissuemnist npz files and move to the ./datasets/medmnist directory

# large-scale datasets
https://www.kaggle.com/c/rsna-pneumonia-detection-challenge
https://www.kaggle.com/datasets/benjaminwarner/resized-2015-2019-blindness-detection-images
https://challenge.isic-archive.com/landing/2019/
https://github.com/owkin/FLamby/blob/main/flamby/datasets/fed_isic2019/README.md
# check tools for preprocessing

Environments

pip install -U pip
pip install -r requirements.txt

Citation

If you find this repository useful in your research, please cite:

@inproceedings{kang2023one,
  title={One-Shot Federated Learning on Medical Data Using Knowledge Distillation with Image Synthesis and Client Model Adaptation},
  author={Kang, Myeongkyun and Chikontwe, Philip and Kim, Soopil and Jin, Kyong Hwan and Adeli, Ehsan and Pohl, Kilian M and Park, Sang Hyun},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={521--531},
  year={2023},
  organization={Springer}
}

Thanks to works below for their implementations which were useful for this work. DeepInversion