[CVPR'21] FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space

Overview

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

  1. Start with a demo for continuous frequency space interpolation among federated clicnets:
    python freq_space_interpolation_demo.py

  1. Prepare the dataset, and then extract the amplitude spectrum of samples in each local client with the function in dataset/prepare_dataset.py:

  2. Organize the data (saved sa npy) 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
    
  3. 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.

Owner
Quande Liu
Medical Image Analysis, Model Robustness & Generalizability, Federated Learning
Quande Liu
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