This BlipForConditionalGeneration model generates realistic radiology reports given an chest X-ray and a clinical indication (e.g. 'RLL crackles, eval for pneumonia').
- Developed by: Nathan Sutton
- Model type: BLIP
- Language(s) (NLP): English
- License: Apache 2.0
- Finetuned from model: Salesforce/blip-image-captioning-large
- Repository: https://github.com/nathansutton/prerad
- Paper: https://medium.com/@nasutton/a-new-generative-model-for-radiology-b687a993cbb
- Demo: https://nathansutton-prerad.hf.space/
Any medical application.
from PIL import Image from transformers import BlipForConditionalGeneration, BlipProcessor # read in the model processor = BlipProcessor.from_pretrained("nathansutton/generate-cxr") model = BlipForConditionalGeneration.from_pretrained("nathansutton/generate-cxr") # your data my_image = 'my-chest-x-ray.jpg' my_indication = 'RLL crackles, eval for pneumonia' # process the inputs inputs = processor( images=Image.open(my_image), text='indication:' + my_indication, return_tensors="pt" ) # generate an entire radiology report output = model.generate(**inputs,max_length=512) report = processor.decode(output, skip_special_tokens=True)
This model was trained by cross-referencing the radiology reports in MIMIC-CXR with the images in the MIMIC-CXR-JPG. None are available here and require a data usage agreement with physionet.
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