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ADA4MIA: Active Domain Adaptation for Medical Image Analysis

• ADA4MIA is a benchmark repo dedicated to enhancing Domain Adaptation and Active Learning in medical image analysis. Our goal is to foster robust model development across varied medical datasets, facilitating straightforward evaluation and comparison of different methods.

• This repository collects various state-of-the-art methods, open-source code, and related datasets for the community. If you are interested, you can push your implementations or ideas to this repo or contact me (📮：hongqiuwang16@gmail.com)(🛰️ Wechat:whqqq7) at any time.

• This project was originally developed for our previous works. Now and future, we are still working on extending it to be more user-friendly and support more approaches to further boost and ease this topic research. The parts of the code for training the source model, generating pseudo-labels, and fine-tuning the target model are provided in our [STDR] project. If you use this codebase in your research, please cite the following works:

     @article{wang2024dual,
      title={Dual-reference source-free active domain adaptation for nasopharyngeal carcinoma tumor segmentation across multiple hospitals},
      author={Wang, Hongqiu and Chen, Jian and Zhang, Shichen and He, Yuan and Xu, Jinfeng and Wu, Mengwan and He, Jinlan and Liao, Wenjun and Luo, Xiangde},
      journal={IEEE Transactions on Medical Imaging},
      year={2024},
      publisher={IEEE}
    }
  
     @inproceedings{wang2024advancing,
       title={Advancing uwf-slo vessel segmentation with source-free active domain adaptation and a novel multi-center dataset},
       author={Wang, Hongqiu and Luo, Xiangde and Chen, Wu and Tang, Qingqing and Xin, Mei and Wang, Qiong and Zhu, Lei},
       booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
       pages={75--85},
       year={2024},
       organization={Springer}
     }

Outline

• [ADA4MIA: Active Domain Adaptation for Medical Image Analysis]
  • 1. Datasets
  • 2. Source Free Active Domain Adaptation
  • 3. Traditional Domain Adaptation
  • 4. Active Learning

1. Datasets

2. Source-Free-Active-Domain-Adaptation

3. Traditional Domain Adaptation

4. Active Learning