This repository provides the code for "Rethinking adversarial domain adaptation: Orthogonal decomposition for unsupervised domain adaptation in medical image segmentation". Our work is accepted by MedIA mia_link.
Fig. 1. Structure of ODADA.
Some important required packages include:
Follow official guidance to install Pytorch.
This Repository contains a toy dataset (HK and BIDMC) for reimplement. You can download a full-version dataset via https://drive.google.com/drive/folders/1KEomtcpTUYCc94nAvEBBsT3vvLnR4rPN?usp=share_link If the data violates privacy, please let us know in time.
To train ODADA for multi-site prostate segmentation, run:
python main.py
Our experimental results are shown in the table:
If you find our work is helpful for your research, please consider to cite:
@article{sun2022rethinking,
title={Rethinking adversarial domain adaptation: Orthogonal decomposition for unsupervised domain adaptation in medical image segmentation},
author={Sun, Yongheng and Dai, Duwei and Xu, Songhua},
journal={Medical Image Analysis},
pages={102623},
year={2022},
publisher={Elsevier}
}