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---
license: apache-2.0
pipeline_tag: image-to-text
---
## generate-cxr
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
## Model Sources
- **Repository:** https://github.com/nathansutton/prerad
- **Paper:** https://medium.com/@nasutton/a-new-generative-model-for-radiology-b687a993cbb
- **Demo:** https://nathansutton-prerad.hf.space/
## Out-of-Scope Use
Any medical application.
## How to Get Started with the Model
```
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[0], skip_special_tokens=True)
```
# Training Details
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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