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

• Abstract
• Introduction
• Requirements
• Datasets
• Usage
• Acknowledgements

In mixed domain semi-supervised medical image segmentation (MiDSS), achieving superior performance under domain shift and limited annotations is challenging. This scenario presents two primary issues: (1) distributional differences between labeled and unlabeled data hinder effective knowledge transfer, and (2) inefficient learning from unlabeled data causes severe confirmation bias. In this paper, we propose the bidirectional correlation maps domain adaptation (BCMDA) framework to overcome these issues. On the one hand, we employ knowledge transfer via virtual domain bridging (KTVDB) to facilitate cross-domain learning. First, to construct a distribution-aligned virtual domain, we leverage bidirectional correlation maps between labeled and unlabeled data to synthesize both labeled and unlabeled images, which are then mixed with the original images to generate virtual images using two strategies, a fixed ratio and a progressive dynamic MixUp. Next, dual bidirectional CutMix is used to enable initial knowledge transfer within the fixed virtual domain and gradual knowledge transfer from the dynamically transitioning labeled domain to the real unlabeled domains. On the other hand, to alleviate confirmation bias, we adopt prototypical alignment and pseudo label correction (PAPLC), which utilizes learnable prototype cosine similarity classifiers for bidirectional prototype alignment between the virtual and real domains, yielding smoother and more compact feature representations. Finally, we use prototypical pseudo label correction to generate more reliable pseudo labels. Empirical evaluations on three public multi-domain datasets demonstrate the superiority of our method, particularly showing excellent performance even with very limited labeled samples.

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.