FedIIC

This is the official implementation for the paper: "FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classification", which is accepted at MICCAI'23 (Early Accept, top 14% in total 2253 submissions).

Brief Introduction

This paper investigates federated learning using class-imbalanced (global) and heterogeneous (local) medical data.

Related Work

• CReFF [paper] [code]
• CLIMB [paper] [code]
• PRR-Imbalance [paper] [code]

Dataset

Please download the ICH dataset from kaggle and preprocess it follow this notebook. Please download the ISIC 2019 dataset from this link.

Requirements

We recommend using conda to setup the environment, See the requirements.txt for environment configuration.

Citation

If this repository is useful for your research, please consider citing:

@inproceedings{Wu2023FedIIC,
  title={FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classification},
  author={Wu, Nannan and Yu, Li and Yang, Xin and Cheng, Kwang-Ting and Yan, Zengqiang},
  booktitle={MICCAI},
  year={2023}
}

Contact

For any questions, please contact 'wnn2000@hust.edu.cn'.