by Quande Liu, Cheng Chen, Jing Qin, Qi Dou, Pheng-Ann Heng.
This repository is for our CVPR 2021 paper 'FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space'.
Start with a demo for continuous frequency space interpolation among federated clicnets:
python freq_space_interpolation_demo.py
Prepare the dataset, and then extract the amplitude spectrum of samples in each local client with the function in dataset/prepare_dataset.py
:
Organize the data (save the data as npy to speed up federated training) and amplitude spectrum of local clients as following structure:
├── dataset
├── client1
├── data_npy
├── sample1.npy, sample2.npy, xxxx
├── freq_amp_npy
├── amp_sample1.npy, amp_sample2.npy, xxxx
├── clientxxx
├── clientxxx
Train the federated learning model with ELCFS:
python train_ELCFS.py
If this repository is useful for your research, please consider citing:
@article{liu2021feddg,
title={FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space},
author={Liu, Quande and Chen, Cheng and Qin, Jing and Dou, Qi and Heng, Pheng-Ann},
journal={The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2021}
}
Some of the code is adapted from SAML and FDA. The datasets used in this paper are downloaded from Prostate and Fundus.
Please contact 'qdliu0226@gmail.com'