iamvazu / covid-xray

Overview

This repository is an implementation of the paper "COVID-MobileXpert: On-Device COVID-19 Screening using Snapshots of Chest X-Ray".

Android App

Our Android App is available here. Please feel free to have a try. You can directly download the snapshots provided in "Test Cases for App" for testing (click on the "View code").

Here are the steps to test with your own CXR images (click on the "View all of README.md"):

• Download and install our App here.
• Download and install Microsoft Office Lens here.
• Display a CXR image on your screen (PACS system or PC).
• Open Microsoft Office Lens and take a snapshot of the CXR image under the "DOCUMENT" mode (adjust border if needed).
• Choose the "GRAYSCALE" filter and save the snapshot.
• Open our App, load the snapshot from the gallery and make the prediction (check the following live demo).

Live demo

CXR Imaging Datasets

• Pre-training Data (108,948 CXR Images)
  • ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases.
• Fine-tuning Data (537 CXR Images)
  • COVID-19 Image Data Collection.
  • RSNA Pneumonia Detection Challenge.
• Fine-tuning data is split into training/validation/testing sets with 125/18/36 images for each class.

Snapshots Dataset

To create a noisy snapshot dataset, we first display the original CXR image on a PC screen and then use Microsoft Office Lens to scan and save the snapshot. Noisy snapshots will be converted to 8-bit gray-scale images. Each clean CXR image from CXR image dataset has a noisy snapshot counterpart, Here is one example:

COVID-MobileXpert Architecture with Three-player KTD Training

Model Training and Evaluation

Codes and learned model parameters are available in the Main folder. Here are the steps for training and testing:

• Put the CXR images in the Dataset folder as the following structure:

Dataset
   train
      clean
      covid
      pneumonia
   test
      clean
      covid
      pneumonia
   validation
      clean
      covid
      pneumonia

• Download the pre-trained model here and save it into RF_model folder.
• Run the .ipynb file for training and testing.

On-device Deployment of the COVID-MobileXpert

We provide the source code for deployment with Pytorch Mobile and Android Studio, which is developed based on this repository. The source code contains an example model, if you want to deploy other models, here are the steps:

• Download the pre-trained models.
• Use the script "TorchScript_converter.py" to convert the model to TorchScript (.pt).
• Put the model under "src/main/assets" folder
• Change the path in 'MainActivity.java' to the current .pt file.
• Build and test.

Results

Evaluation of COVID-19 Screening Performance using both CXR Images and Noisy Snapshots.

Dependencies

• Python 3.7
• Pytorch 1.3

Citation

Xin Li, Chengyin Li and Dongxiao Zhu
COVID-MobileXpert: On-Device COVID-19 Screening using Snapshots of Chest X-Ray, arXiv:2004.03042, 2020  
https://github.com/xinli0928/COVID-Xray

@misc{li2020covidmobilexpert,
    title={COVID-MobileXpert: On-Device COVID-19 Screening using Snapshots of Chest X-Ray},
    author={Xin Li and Chengyin Li and Dongxiao Zhu},
    year={2020},
    eprint={2004.03042},
    archivePrefix={arXiv},
    primaryClass={eess.IV}
}