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UniDataPro
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mammography-segmentation

Tasks:
Image Segmentation
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Tags:
dicom
medicine
healthcare
medical imaging
xray
health
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id
float64
1.1
2.4
dcm_filepath
stringclasses
8 values
ann_filepath
stringclasses
8 values
1.1
https://drive.google.com/file/d/1B0ONxveXrKLFfllAPUVvHN4kyyFep6CR/view?usp=drive_link
https://drive.google.com/file/d/12jpuA0_xnneIXyXrVfbEQFjTW4VtUKxj/view?usp=drive_link
1.2
https://drive.google.com/file/d/1fZVSFTbn4wf_i0YCul9wsBvunNowDP7O/view?usp=drive_link
https://drive.google.com/file/d/1s37wL6Z_WiBihS-9_3yg8xk8wmtelku9/view?usp=drive_link
1.3
https://drive.google.com/file/d/1gRTOjHnkPKLSWWEaOG9BsA-Z4DhmZV9S/view?usp=drive_link
https://drive.google.com/file/d/1M5qi5E1k71gxIA8a0CRMfClC1MeKZ3xX/view?usp=drive_link
1.4
https://drive.google.com/file/d/1uEOSU2-xfyM3-VrcISAzjYauIBWIw0uK/view?usp=drive_link
https://drive.google.com/file/d/1HALMvxTIwBy4ZBbBt7Iv-_o4RLUL0WjR/view?usp=drive_link
2.1
https://drive.google.com/file/d/1li5-KI4IhkrunTs4bq2f8MIS3u70Yxhy/view?usp=drive_link
https://drive.google.com/file/d/10rd6eY80QETuYsjBecWheLMWWtGPiPrK/view?usp=drive_link
2.2
https://drive.google.com/file/d/1bD4ZNX7cF70zkA2BZpsz1tDTqsDz5czE/view?usp=drive_link
https://drive.google.com/file/d/1hEULlrnA70AVdxgGu-gQbQNBEcZfgx8J/view?usp=drive_link
2.3
https://drive.google.com/file/d/1Z934GXHPlOROjl-agMdgTbuv38D904Bs/view?usp=drive_link
https://drive.google.com/file/d/1t2qDKqaGkzNYrkytzoDRM09fhHvdwA3I/view?usp=drive_link
2.4
https://drive.google.com/file/d/1majb70u4LASJY4ep_x1PJhhUA9cQnseE/view?usp=drive_link
https://drive.google.com/file/d/1V2vdA151KigO757fwEfJYNlIEYfeEE6C/view?usp=drive_link

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

Example of the data

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 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 provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects

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