BCMDA: Bidirectional Correlation Maps Domain Adaptation for Mixed Domain Semi-Supervised Medical Image Segmentation (Neural Networks)
Pytorch implementation of our method for Neural Networks paper: "BCMDA: Bidirectional Correlation Maps Domain Adaptation for Mixed Domain Semi-Supervised Medical Image Segmentation".Paper Link
Official code for "BCMDA: Bidirectional Correlation Maps Domain Adaptation for Mixed Domain Semi-Supervised Medical Image Segmentation".
This repository is based on PyTorch 2.1.0, CUDA 12.1, and Python 3.8. All experiments in our paper were conducted on an NVIDIA GeForce RTX 4090 GPU with an identical experimental setting under Ubuntu 22.
Prostate, Fundus, and M&Ms datasets can be downloaded from MiDSS.
The ./data folder illustrates the data format.
To train a model,
python ./Fundus_train.py --overwrite --lb_domain ... --data_path ../data/Fundus #for Fundus training
python ./Prostate_train.py --overwrite --lb_domain ... --data_path ../data/ProstateSlice #for Prostate training
python ./MNMS_train.py --overwrite --lb_domain ... --data_path ../data/mnms #for M&Ms training
To test a model,
python ./test.py --overwrite --lb_domain ... --data_path ../data/Fundus --dataset fundus --save_name ... #for Fundus testing
python ./test.py --overwrite --lb_domain ... --data_path ../data/ProstateSlice --dataset prostate --save_name ... #for Prostate testing
python ./test.py --overwrite --lb_domain ... --data_path ../data/mnms --dataset MNMS --save_name ... #for M&Ms testing
If our BCMDA is useful for your research, please consider citing:
@article{song2026bcmda,
title={BCMDA: Bidirectional Correlation Maps Domain Adaptation for Mixed Domain Semi-Supervised Medical Image Segmentation},
author={Song, Bentao and Huang, Jun and Wang, Qingfeng},
journal={Neural Networks},
pages={108877},
year={2026},
publisher={Elsevier}
}
Our code is largely based on MiDSS and SSL4MIS. Thanks for these authors for their valuable work, hope our work can also contribute to related research.