by Quande Liu, Hongzheng Yang, Qi Dou, Pheng-Ann Heng.
Edited by Yasin Tarakçı
Pytorch implementation for MICCAI 2021 paper "Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching"
-
Clone the repository and go to the repository root directory:
git clone https://github.com/ysntrkc/FedIRM.git cd FedIRM
-
Create a conda environment and activate it:
conda env create -f environment.yml conda activate fedIRM
-
Download datasets from following sources:
-
Extract the datasets to
data/
folder and place the content into folders like this:- ISIC 2019:
data/isic2019/
- HAM10000:
data/ham10000/
- RSNA Intracranial Hemorrhage Detection:
data/rsna/
- ISIC 2019:
-
Go to data folder from terminal with
cd data
and runpython prepare_data.py
to prepare the datasets.
-
Go to the
src
folder withcd src
and runpython main.py
to train the model. -
You can see the different options in
src/options.py
and change the parameters as you want. -
You can see the example run commands in
src/example_run.sh
file.
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}
}
Please contact '[email protected]' or '[email protected]'
If you have questions about this version of the code, you can also contact '[email protected]'