ludolara
/

vit-COVID-19-severity

Image Classification

Inference Endpoints

Model card Files Files and versions Community

ludolara commited on Apr 29, 2023

Commit

7f48d03

•

1 Parent(s): 1025210

Update README.md

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -14,7 +14,7 @@ tags:
 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

 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