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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 (📮:[email protected])(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}
    }
  
    @article{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},
      journal={arXiv preprint arXiv:2406.13645},
      year={2024}
    }

Outline

1. Datasets

Short name Paper Source Data Link
STDR Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple Hospitals TMI 2024 [dataset]

2. Source-Free-Active-Domain-Adaptation

Short name Paper Source Code Link
STDR Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple Hospitals TMI 2024 [code]
CUP Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center Dataset MICCAI 2024 [code]
UGTST An Uncertainty-guided Tiered Self-training Framework for Active Source-free Domain Adaptation in Prostate Segmentation MICCAI 2024 [code]

3. Traditional Domain Adaptation

Short name Paper Source Code Link
AdaptSeg Learning to adapt structured output space for semantic segmentation CVPR 2018 [code]
AdvEnt Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation CVPR 2019 [code]
UncertainDA Uncertainty reduction for model adaptation in semantic segmentation CVPR 2021 [code]
Tent Tent: Fully test-time adaptation by entropy minimization ICLR 2021 [code]
DPL Source-free domain adaptive fundus image segmentation with denoised pseudo-labeling MICCAI 2021 [code]
FSM Source free domain adaptation for medical image segmentation with fourier style mining MIA 2022 [code]
AdaMI Source-free domain adaptation for image segmentation MIA 2022 [code]
CBMT Source-free domain adaptive fundus image segmentation with class-balanced mean teacher MICCAI 2023 [code]
CPR Context-Aware Pseudo-label Refinement for Source-Free Domain Adaptive Fundus Image Segmentation MICCAI 2023 [code]

4. Active Learning

Short name Paper Source Code Link
AADA Active adversarial domain adaptation WACV 2020 [code]
MADA Multi-anchor active domain adaptation for semantic segmentation ICCV 2021 [code]
MHPL Mhpl: Minimum happy points learning for active source free domain adaptation CVPR 2023 [code]
CLAUS Hybrid active learning via deep clustering for video action detection CVPR 2023 [code]
ActiveFT Active finetuning: Exploiting annotation budget in the pretraining-finetuning paradigm CVPR 2023 [code]
BAL Bal: Balancing diversity and novelty for active learning TPAMI 2023 [code]
ALFREDO ALFREDO: Active Learning with FeatuRe disEntangelement and DOmain adaptation for medical image classification MIA 2024 [None]