Create README.md

README.md

@@ -0,0 +1,69 @@
+---
+license: apache-2.0
+pipeline_tag: image-to-text
+---
+# Model Card for Model ID
+
+Trained on the MIMIC-CXR chest x-rays.
+
+
+
+# Model Details
+
+## Model Description
+
+This 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 [optional]:** Salesforce/blip-image-captioning-large
+
+## Model Sources [optional]
+
+- **Repository:** https://github.com/nathansutton/prerad
+- **Paper [optional]:** https://medium.com/me/stats/post/b687a993cbb
+- **Demo [optional]:** https://medium.com/me/stats/post/b687a993cbb
+
+
+## Direct Use
+
+Upload a chest x-ray in a JPG format along with a clinical indication (e.g. 'RLL crackles, eval for pneumonia').  It will generate a realistic looking radiology report.
+
+## 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
+
+## Training Data
+
+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.