SCUT-AILab / CoUDA

This project provides the source code for “Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis (IEEE TIP 2020)”.

CoUDA

We provide the original implementation for “Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis (IEEE TIP 2020)”.

Dependencies

Python 2.7
Tensorflow-gpu 1.12.0
Opencv-python
Numpy 1.15.4

Usage

Please use this code after downloading the dataset and the model.

Dataset and Model

Dataset: The attached dataset is Office-31 corrupted by the label noise rate 0.1.
Please download the dataset [here] and put the dataset file (domain_adaptation_images) in the main directoty.

Model: Plase download the trained model [here], and put the model file (dual_log_office) in the main directory.

Training

Take the model adapted from webcam to amazon as an example. There are two steps:

1. Set the environment file: vi src_office/conf/local_nn_dual.yml
2. CUDA_VISIBLE_DEVICES=0 python src_office/train_dual.py >> ./dual_log_office/ours/webcam_2_amazon.txt src_office/conf/local_mn_dual.yml

Test

Take the model adapted from dslr to webcam as an example. There are two steps:

1. Set the environment file: vi src_office/conf/predict_dual.yml
2. CUDA_VISIBLE_DEVICES=0 python src_office/predict_dual.py >> ./dual_log_office/ours/dslr_2_webcam/test.txt src_office/conf/predict_dual.yml

Citation:

If you use this code and dataset, please cite:

@article{zhang2020collaborative,
  title={Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis},
  author={Zhang, Yifan and Wei, Ying and Wu, Qingyao and Zhao, Peilin and Niu, Shuaicheng and Huang, Junzhou and Tan, Mingkui},
  journal={IEEE Transactions on Image Processing},
  year={2020}
}