X-Raydar Official Repository

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 for the NLP system
   1. add robertax1.0.pt into src/model/
   2. add pytorch_model.bin into src/model/robertax_pretrained/
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, tokenizer = predict.build_model()
filename = '../demo_data/test1.txt'

with open(filename) as file:
    report = file.read()
    input_ids, attention_masks = predict.doc_to_torch([report], tokenizer)
    predictions = predict.main(input_ids, attention_masks, model)

Authors

• @wmgdatascience
• @yziw
• @granmirupa