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The dataset used in COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19

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# <center>COVID-DA: Deep Domain Adaptation from <br> Typical Pneumonia to COVID-19</center>
We provide the **COVID-DA** dataset for domain adaptation from typical pneumonia to COVID-19. The paper is available [here](https://arxiv.org/pdf/2005.01577.pdf).

# Dataset
The descriptions for the **COVID-DA** dataset are presented below. We first provide a link to download the dataset. Next, the data statistics and usage of the dataset will be introduced.
## Download
- The dataset in this paper is available [here](https://drive.google.com/file/d/1w2brbYLn1s1hvmLkKKsBsm1mCbz4F512/view?usp=sharing).

## Data Composition
To make up this dataset, we collected and integrated the following open-source datasets:
https://github.com/ieee8023/covid-chestxray-dataset  
https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data  
https://www.kaggle.com/darshan1504/covid19-detection-xray-dataset  
https://www.kaggle.com/andrewmvd/convid19-X-rays  
https://www.kaggle.com/nabeelsajid917/covid-19-x-ray-10000-images  
https://www.kaggle.com/usmantahirkiani/covid19-vs-healthy-xray  
https://www.kaggle.com/tarandeep97/covid19-normal-posteroanteriorpaxrays

- Statistics of the dataset are shown as follow:\
![data statistic](https://raw.githubusercontent.com/qiuzhen8484/COVID-DA/master/data.png "statistics of the dataset")\
Pneumonia ("all_data_pneumonia" sub-directory) serves as the source domain and COVID-19 ("all_data_covid" sub-directory) serves as the target domain.

- You can refer to the [paper](https://arxiv.org/pdf/2005.01577.pdf) for more details about the dataset.

## Usage
- In the directory `./data`, there are two `.pkl` files which record the image lists and its corresponding labels. Specifically, an image and its label is stored in a tuple (image_name, label). "1" denotes class "pneumonia" and class "COVID-19" in source and target domain, respectively, while "0" denotes class "normal". You can read the data list following the below manner:
  - for the source domain (Pneumonia):
  ```
        with open('./data/pneumonia_task.pkl', 'rb') as f:
            train_dict = pickle.load(f)
        train_list = train_dict['train_list'] # train sub-directory
        val_list = train_dict['val_list'] # test sub-directory
  ```
  - for the target domain (COVID-19):
  ```
        with open('./data/COVID-19_task.pkl', 'rb') as f:
            train_dict = pickle.load(f)
        train_list_labeled = train_dict['train_list_labeled'] # labeled data (train sub-directory)
        train_list_unlabeled = train_dict['train_list_unlabeled'] # unlabeled data (train sub-directory)
        val_list = train_dict['val_list'] # val sub-directory
        test_list = train_dict['test_list'] # test sub-directory
  ```
  For convenience, we provide `.pkl` files for both python 2 and 3, respectively.

- According to the image lists, you can load images using Pillow:
  ```
        # e.g., for the source domain (Pneumonia)
        for img_tup in train_list:
            img = PIL.Image.open(os.path.join('all_data/all_data_pneumonia', 'train', img_tup[0])
            label = img_tup[1]
  ```

# Citation
If you find the **COVID-DA** dataset useful, please cite the following paper:
```
@article{zhang2020covidda,
    title={COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19},
    author={Yifan Zhang and Shuaicheng Niu and Zhen Qiu and Ying Wei and Peilin Zhao and Jianhua Yao and Junzhou Huang and Qingyao Wu and Mingkui Tan},
    journal={arXiv},
    year={2020},
}
```

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