Update README.md

README.md

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-license: cc-by-nc-nd-4.0
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+---
+license: cc-by-nc-nd-4.0
+task_categories:
+- image-segmentation
+tags:
+- dicom
+- medicine
+- healthcare
+- medical imaging
+- xray
+- health
+- computer vision
+- mammography
+size_categories:
+- 1K<n<10K
+---
+# Mammography X-rays, DICOM Data and Segmentation
+
+Dataset comprises over **3,000** studies featuring detailed mammogram x-ray that capture **14** distinct pathologies. This extensive dataset is formatted in **DICOM**, ensuring compatibility with a wide range of **medical imaging software**.
+
+By utilizing this dataset, researchers can explore advanced **segmentation techniques** and evaluate the **classification performance** of their models. -  **[Get the data](https://unidata.pro/datasets/mammography-segmentation/?utm_source=huggingface&utm_medium=cpc&utm_campaign=mammography-segmentation)**
+
+# Example of the data
+![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2F12611a74e8cf66fd9ff06491b7448644%2FFrame%20171.png?generation=1732669004646494&alt=media)
+
+The dataset features annotations and segmentation results, with lung segmentations provided by radiologists and medical experts. These annotations are useful for training deep learning algorithms to improve classification performance in identifying common diseases and lung abnormalities.
+### 13 classes of labeling:
+1. Id
+2. classId
+3. Description
+4. geometryType
+5. labelerLogin
+6. createdAt
+7. updatedAt
+8. tags
+9. classTitle
+10. points(exterior, interior)
+
+Each study includes meticulously crafted segmentation masks that delineate various breast lesions, including breast masses, tumors, and microcalcifications.
+# 💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at [https://unidata.pro](https://unidata.pro/datasets/mammography-segmentation/?utm_source=huggingface&utm_medium=cpc&utm_campaign=mammography-segmentation) to discuss your requirements and pricing options.
+
+# About data
+**Example of pathologies:**
+- Skin thickening
+- Papilloma
+- Malignant lesion 
+- Benign lesion
+- Malignant calcification cluster
+- Calcified vessel
+- and etc.
+
+The dataset is instrumental in enhancing the segmentation performance of algorithms, thereby contributing to more accurate cancer detection and improved screening mammography practices.
+
+# 🌐 [UniData](https://unidata.pro/datasets/mammography-segmentation/?utm_source=huggingface&utm_medium=cpc&utm_campaign=mammography-segmentation) provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects