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FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space
by Quande Liu, Cheng Chen, Jing Qin, Qi Dou, Pheng-Ann Heng.
Introduction
This repository is for our CVPR 2021 paper 'FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space'.
Usage
- 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
Citation
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}
}
Acknowledgement
Some of the code is adapted from SAML and FDA. The datasets used in this paper are downloaded from Prostate and Fundus.
Questions
Please contact 'qdliu0226@gmail.com'