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README.md

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-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 scale ranging from 1 &#x2B50; star to 6  &#x2B50;&#x2B50;&#x2B50;&#x2B50;&#x2B50;&#x2B50; stars inspyred 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 as 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)
 
+
+> [!NOTE]
+The model here is not the original model presented in the paper. Instead it is trained on the individual radiologists' ratings. This way the model has a chance to learn the variance between radioligsts' estimates. It outperforms the model presented in the paper on the test set.
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 ## Installation
 
 With a Python 3.9+ environment, you can install `deep_quality_estimation` directly from [PyPI](https://pypi.org/project/deep_quality_estimation/):