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<!-- # Source free domain adaptation for medical image segmentation with fourier style mining # Overview Unsupervised domain adaptation (UDA) aims to exploit the knowledge learned from a labeled source dataset to solve similar tasks in a new unlabeled target domain. Existing UDA techniques typically assume that samples from source and target domains are freely accessible during the training. However, it may be impractical to access source images due to privacy concerns, especially in medical imaging scenarios with the patient information. To tackle this issue, we devise a novel source free domain adaptation framework with fourier style mining, where only a well-trained source segmentation model is available for the adaptation to the target domain. Our framework is composed of two stages: a generation stage and an adaptation stage. In the generation stage, we design a Fourier Style Mining (FSM) generator to inverse source-like images through statistic information of the pretrained source model and mutual Fourier Transform. These generated source-like images can provide source data distribution and benefit the domain alignment. In the adaptation stage, we design a Contrastive Domain Distillation (CDD) module to achieve feature-level adaptation, including a domain distillation loss to transfer relation knowledge and a domain contrastive loss to narrow down the domain gap by a self-supervised paradigm. Besides, a Compact-Aware Domain Consistency (CADC) module is proposed to enhance consistency learning by filtering out noisy pseudo labels with shape compactness metric, thus achieving output-level adaptation. Extensive experiments on cross-device and cross-centre datasets are conducted for polyp and prostate segmentation, and our method delivers impressive performance compared with state-of-the-art domain adaptation methods. # Installation # Getting Started -->

Source Free Domain Adaptation for Medical Image Segmentation with Fourier Style Mining (MIA 2022)

This is the official PyTorch implementation of FSM (Fourier Style Mining) (MIA 2022).

Source Free Domain Adaptation for Medical Image Segmentation with Fourier Style Mining[Paper]

Chen Yang, Xiaoqing Guo, Zhen Chen, Yixuan Yuan

<div align="center"> <img src="figs/framework.png"/> </div>

Get Started

Environment

Install dependencies

pip install -r requirements.txt

Datasets Preparation

EndoScene and ETIS-Larib

(1) Download the EndoScene and ETIS-Larib dataset.

(2) Put the data in the corresponding folders. The dataset files are organized as follows.

SFDA-FSM
├── data
│   ├── EndoScene
│   │   ├── images
│   │   │   ├── [case_id].png
│   │   ├── labels
│   │   │   ├── [case_id].png
│   ├── ETIS-Larib
│   │   ├── images
│   │   │   ├── [case_id].png
│   │   ├── labels
│   │   │   ├── [case_id].png

(3) Split dataset into training set and test set as follows.

python preprocess.py

Training

Generation Stage

(1) Generate source-like images with pretrained source model as follows.

python tools/domain_inversion.py

(2) Visualization of source-like images.

<div align="center"> <img src="figs/generation.jpg"/> </div>

Adaptation Stage

python tools/train_adapt.py

Testing

python tools/test.py

Citation

If you find this project useful, please consider citing:

@article{yang2022source,
  title={Source free domain adaptation for medical image segmentation with fourier style mining},
  author={Yang, Chen and Guo, Xiaoqing and Chen, Zhen and Yuan, Yixuan},
  journal={Medical Image Analysis},
  volume={79},
  pages={102457},
  year={2022},
  publisher={Elsevier}
}