Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching

by Quande Liu, Hongzheng Yang, Qi Dou, Pheng-Ann Heng.

Introduction

Pytorch implementation for MICCAI 2021 paper "Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching"

Usage

1. create conda environment

   conda create -n fedIRM python=3.7
   conda activate fedIRM
   

2. Install dependencies:

   1. install pytorch==1.8.0 torchvision==0.9.0 (via conda, recommend)

3. download the dataset from kaggle and preprocess it follow this notebook. You can download the preprocessed the dataset from notebook.

4. modify the corresponding data path in options.py

5. train the model

   python train_main.py
   

Citation

If this repository is useful for your research, please cite:

@article{liu2021federated,
  title={Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching},
  author={Liu, Quande and Yang, Hongzheng and Dou, Qi and Heng, Pheng-Ann},
  journal={International Conference on Medical Image Computing and Computer Assisted Intervention},
  year={2021}
}  

Questions

Please contact 'qdliu0226@gmail.com' or 'hzyang05@gmail.com'