add star

README.md

@@ -7,14 +7,19 @@
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
 <!-- [![codecov](https://codecov.io/gh/BrainLesion/deep_quality_estimation/graph/badge.svg?token=A7FWUKO9Y4)](https://codecov.io/gh/BrainLesion/deep_quality_estimation) -->
 
-Quality prediction for brain tumor segmentation on scale ranging from 1 to 6 stars &#x2B50;.  
-Can be used to estimate the quality of a segmentation for evaluation purposes or as e.g. as part of a loss function during model training.
+Quality prediction for brain tumor segmentation on a scale ranging from &#x2B50; 1 star to  &#x2B50;&#x2B50;&#x2B50;&#x2B50;&#x2B50;&#x2B50; 6 stars inspired by the paper [**Deep Quality Estimation: Creating Surrogate Models for Human Quality Ratings**](https://arxiv.org/abs/2205.10355).  
+This can be used to estimate the quality of a BraTS glioma segmentation for evaluation purposes or, e.g., as part of a loss function during model training.
 
 > [!NOTE]  
 > This package expects images in atlas space and segmentation labels in brats style, i.e.
 > - `label 1` is the necrotic and non-enhancing tumor core
 > - `label 2` is the peritumoral edema
-> - `label 3` is the GD-enhancing tumor (used to be `label 4` in older data, both are supported)
+> - `label 3` is the GD-enhancing tumor (used to be `label 4` in older data; both are supported)
+
+
+> [!NOTE]
+The model here is not the original model presented in the paper. Instead, it is trained based on individual radiologists' ratings. This way, the model has a chance to learn the variance between radiologists' estimates. It outperforms the model presented in the paper on the test set.
+
 
 ## Installation