/x-raydar-cv

Official repository for the paper "Development of a freely accessible deep learning platform for comprehensive chest X-ray reading: a retrospective multicenter study in the UK"

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X-Raydar Official Repository

x-raydar

Development of a freely accessible deep learning platform for comprehensive chest X-ray reading: a retrospective multicenter study in the UK

The code in this repository refers to the paper published on "The Lancet Digital Health" journal.

Testing the model

NOTE: This is not for clinical use

  1. Clone this repository
  2. Register on x-raydar official webpage and accept our terms and conditions
  3. Download the network weights and add them in \src\model_20210820_XNet38MS\model_weights
  4. Use the DICOM in \demo_data to test the model

In order to download the pretrained network weights you will need to first register on

  https://www.x-raydar.info/

and accept our terms and conditions.

Code Example

model = predict.build_model()

filename = '../demo_data/04f72062c19d9cd7a55519708aa2cc58b5e52b52' # test DICOM

dicom = pydicom.read_file(filename)
image_original = dicom_utils.img_clean(dicom)
predict.main(image_original, model)

Contact

For questions, suggestions, or collaborations, please contact Giovanni Montana at g.montana@warwick.ac.uk.