https://github.com/caiyu6666/DDAD-ASR

[MedIA'2023] Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical images
https://github.com/caiyu6666/DDAD-ASR

anomaly-detection deep-learning pytorch

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[MedIA'2023] Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical images

• Host: GitHub
• URL: https://github.com/caiyu6666/DDAD-ASR
• Owner: caiyu6666
• License: mit
• Created: 2022-08-09T03:35:26.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-03-17T10:31:10.000Z (over 1 year ago)
• Last Synced: 2024-07-31T20:40:46.067Z (3 months ago)
• Topics: anomaly-detection, deep-learning, pytorch
• Language: Python
• Homepage:
• Size: 6.46 MB
• Stars: 44
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• Unsupervised-Anomaly-Detection-in-Brain-MRI - [Code

README

        


  
Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical images

  


    Yu Cai, Hao Chen, Xin Yang, Yu Zhou, Kwang-Ting Cheng

  

  


Paper

  


## Visualization on Med-AD



From top to bottom: original image, ![](http://latex.codecogs.com/svg.latex?\mathcal{A}_{rec}), DDAD-![](http://latex.codecogs.com/svg.latex?\mathcal{A}_{intra}), DDAD-![](http://latex.codecogs.com/svg.latex?\mathcal{A}_{inter}), DDAD-![](http://latex.codecogs.com/svg.latex?\mathcal{R}_{dual}).

## Data Preparation

### Option 1

Download the well-processed Med-AD benchmark from: [Google Drive](https://drive.google.com/file/d/1ijdaVBNdkYP4h0ClYFYTq9fN1eHoOSa6/view?usp=sharing) | [OneDrive](https://hkustconnect-my.sharepoint.com/:u:/g/personal/ycaibt_connect_ust_hk/EdCbKrjjRMlKi-1AotcAfkoB_jmbTQ2gnQChltgh7l8xVQ?e=t17t2S). 


(The benchmark is organized using 4 public datasets, and should be **only applied for academic research**.)

### Option 2

Organize the Med-AD benchmarks manually follow the [guidance](https://github.com/caiyu6666/DDAD-ASR/tree/main/data).

## Environment

- NVIDIA GeForce RTX 3090

- Python 3.10

- Pytorch 1.12.1

### Packages

```

conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch -c nvidia

pip install pillow

pip install joblib

pip install pydicom

pip install opencv-python

pip install scikit-learn

pip install tensorboard

pip install matplotlib 

pip install tqdm

```

## Train and Evaluate

All scripts are available in `scripts/`, and configuration files are in `cfgs/`.

Train and evaluate the method on RSNA dataset using AE as the backbone: `./scripts/run_rsna_ae.sh`

```python

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

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

python main.py --config cfgs/RSNA_AE.yaml --mode a;  # ensemble 3 networks for module a (UDM)

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

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

python main.py --config cfgs/RSNA_AE.yaml --mode b;  # ensemble 3 networks for module b (NDM)

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

python main.py --config cfgs/RSNA_AE.yaml --mode r;

python main.py --config cfgs/RSNA_AE.yaml --mode eval_r;

```

Similarly, for training/evaluating on other datasets using other backbones, the following commands can be used:

```

./scripts/run_rsna_ae.sh

./scripts/run_rsna_memae.sh

./scripts/run_rsna_aeu.sh

./scripts/run_vin_ae.sh

./scripts/run_vin_memae.sh

./scripts/run_vin_aeu.sh

./scripts/run_brain_ae.sh

...

./scripts/run_lag_ae.sh

...

```

The trained models and results are available [here](https://github.com/caiyu6666/DDAD-ASR/releases/tag/downloads).

## Qualitative Analysis

### AS histograms

 

## Contact

If any questions, feel free to contact: [[email protected]](mailto:[email protected])

## Acknowledgement

We really appreciate these wonderful open-source codes and datasets!

### Codes

1. https://github.com/dbbbbm/UAE

2. https://github.com/donggong1/memae-anomaly-detection

### Datasets

1. [RSNA Pneumonia Detection Challenge dataset](https://www.kaggle.com/c/rsna-pneumonia-detection-challenge)

2. [Vin-BigData Chest X-ray Abnormalities Detection dataset (VinDr-CXR)](https://www.kaggle.com/c/vinbigdata-chest-xray-abnormalities-detection)

3. [Brain Tumor MRI dataset](https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset)

4. [Large-scale Attention-based Glaucoma (LAG) dataset](https://github.com/smilell/AG-CNN)

## Citation

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

```

@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},

}

@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}

}

```