Update README.md

README.md

@@ -8,4 +8,16 @@ library_name: transformers
 pipeline_tag: image-classification
 tags:
 - medical
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+---
+
+## Diagnosing Medical Images for COVID-19 Severity (Regression task)
+
+The COVID-19 pandemic has strained healthcare resources and prompted discussion about how machine learning can alleviate physician burdens and contribute to diagnosis. Chest x-rays (CXRs) are used for diagnosis of COVID-19, but few studies predict the severity of a patient’s condition from CXRs. 
+In this study, we produce a large COVID severity dataset by merging three sources and investigate the efficacy of transfer learning vision transformers (ViTs) in severity regression task. 
+The ViT had the best regression results, with an MSE of 0.5135. Code developed in this project is available at https://github.com/stwhitfield/ift6759 project.
+
+## Model description
+
+We were fine-tuned the Vision Transformer (ViT) from HuggingFaces on our training set for 300 epochs using an AdamW optimizer with a learning rate of 0.00001 and a batch size of 10. 
+Early stopping was applied to the model to prevent over-fitting.
+For ViT, training stopped after 70 epochs. The only difference between the preprocessed data in the ViT and the other models was that it normalized to a tensor image with mean and standard deviation during data augmentation.