Update README.md

README.md

@@ -33,7 +33,7 @@ This is v2.0.0 of our dataset! Look for v1.0.0 under tags for the original publi
 
 **Breast MRI** plays a pivotal role in breast cancer detection, diagnosis, and treatment planning. However, most existing segmentation models fail to distinguish between the **left and right breasts**, limiting their usefulness in tasks such as **unilateral classification, response evaluation, or post-mastectomy follow-up**.
 
-In this work, we introduce the **first publicly available large-scale dataset with explicit left and right breast segmentation labels**, comprising **over 17,000 3D MRI scans**. Accompanying this dataset is a [**robust nnU-Net–based segmentation model**](https://huggingface.co/ykirchhoff/BreastDividerModel), trained specifically to identify and separate left and right breast regions in clinical MRI data. This resource provides a foundation for developing anatomically aware AI models and enables large-scale pretraining for downstream breast MRI tasks. Additionally, in V2 we provide over 3,000 classification targets for healthy, benign, and malignant breasts, as well as approximately 467 segmentation masks. 
+In this work, we introduce the **first publicly available large-scale dataset with explicit left and right breast segmentation labels**, comprising **over 17,000 3D MRI scans**. Accompanying this dataset is a [**robust nnU-Net–based segmentation model**](https://huggingface.co/ykirchhoff/BreastDividerModel), trained specifically to identify and separate left and right breast regions in clinical MRI data. This resource provides a foundation for developing anatomically aware AI models and enables large-scale pretraining for downstream breast MRI tasks. Additionally, in V2 we provide over 3,000 classification targets for healthy, benign, and malignant breasts, as well as 467 segmentation masks. 
 
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