Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation

Official pytorch implementation of the paper A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation.
This paper presents a solution to the cross-domain adaptation problem for 2D surgical image segmentation, explicitly considering the privacy protection of distributed datasets belonging to different centers.

Prerequisites

• Python 3.8
• PyTorch 1.7.1
• CUDA 10.1

Installation

First of all, clone the repo:

    git https://github.com/bhattarailab/federated-da.git

All the required python packages can be installed with:

    cd federated-da
    pip install -r requirements.txt

Training

For training, first put training images and corresponding segmentation maps in separate directories and update the train.py file as required.

Then start training with the following command:

    python3 train.py

Results

Qualitative comparison between the proposed method with its baseline methods.

Citation

@article{subedi2023client,
  title={A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation},
  author={Subedi, Ronast and Gaire, Rebati Raman and Ali, Sharib and Nguyen, Anh and Stoyanov, Danail and Bhattarai, Binod},
  journal={arXiv preprint arXiv:2306.08720},
  year={2023}
}