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DDAD (MICCAI 2022)

Our significant extension version of DDAD, named DDAD-ASR, is available at https://github.com/caiyu6666/DDAD-ASR

This is the PyTorch implementation of our paper:

Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays
Yu Cai, Hao Chen, Xin Yang, Yu Zhou, Kwang-Ting Cheng.
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022, Early Accept.

Requirements

  • Python 3.6
  • Pytorch 1.7.0
  • tensorboard 2.5.0
  • pillow 6.1.0
  • pydicom 2.3.0 (for data preprocessing)

Data Preparation

Option 1

Download the well-processed Med-AD benchmark from: Google Drive | OneDrive.
(The benchmark is organized using 4 public datasets including RSNA and VinDr-CXR, and should be only applied for academic research.)

Option 2

  1. Download the training dataset of RSNA Pneumonia Detection Challenge and VinBigData Chest X-ray Abnormalities Detection challenge. Note that we only use their training set as labels of testing set are not available.
  2. Use data/preprocess.py to preprocess the two datasets respectively. The output files should be *.png.
  3. Move the repartition files rsna_data.json and vin_data.json to corresponding data roots and rename to data.json.

The final structure of datasets should be as following:

├─DATA_PATH
│ ├─rsna-pneumonia-detection-challenge   # data root of RSNA dataset
│ │ ├─train_png_512   # preprocessed images of rsna dataset 
│ │ │ ├─xxx.png
│ │ │ ├─ ......
│ │ ├─data.json   # repartition file of rsna dataset (renamed from "rsna_data.json")
│ ├─VinCXR   # data root of VinBigData dataset
│ │ ├─train_png_512   # preprocessed images of VinBigData dataset
│ │ │ ├─xxx.png
│ │ │ ├─ ......
│ │ ├─data.json   # repartition file of VinBigData dataset (renamed from "vin_data.json")

The data.json is a dictionary that storing the data repartition information:

{
  "train": {
    "0": ["*.png", ], # The known normal images for one-class training
    "unlabeled": {
          "0": ["*.png", ], # normal images used to build the unlabeled dataset
    	  "1": ["*.png", ]  # abnormal images used to build the unlabeled dataset
    }
  },
  
  "test": {
  	"0": ["*.png", ],  # normal testing images
  	"1": ["*.png", ]  # abnormal testing images
  }
}

Train and Evaluate

Train the reconstruction network for module A. (Repeat K=3 times to obtain an ensemble of K networks.)

python main.py --config cfgs/RSNA_AE.yaml --mode a

Train the reconstruction network for module B. (Repeat K=3 times to obtain an ensemble of K networks.)

python main.py --config cfgs/RSNA_AE.yaml --mode b

Evaluation

python main.py --config cfgs/RSNA_AE.yaml --mode eval

Each single reconstruction network can also be tested for comparison.

python main.py --config cfgs/RSNA_AE.yaml --mode test

In total, you can directly excute ./train_eval.sh to train and evaluate the DDAD (AE) on RSNA dataset.

Different configuration files can be used for experiments on different datasets and different basic networks.

AUC under different AR of unlabeled dataset

Experiments on RSNA dataset. AE is the basic network.

Histograms of anomaly scores

Citation

If this work is helpful for you, please cite our paper:

@inproceedings{cai2022dual,
  title={Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays},
  author={Cai, Yu and Chen, Hao and Yang, Xin and Zhou, Yu and Cheng, Kwang-Ting},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={584--593},
  year={2022},
  organization={Springer}
}

@article{CAI2023102794,
title = {Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical images},
journal = {Medical Image Analysis},
volume = {86},
pages = {102794},
year = {2023},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2023.102794},
author = {Yu Cai and Hao Chen and Xin Yang and Yu Zhou and Kwang-Ting Cheng},
}

Contact

If you have any question, feel free to email Yu Cai.