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<p>Breast cancer presents the second largest cancer risk in the world to women. Early detection of cancer has been shown to be effective in reducing mortality. Population screening programs schedule regular mammography imaging for participants, promoting early detection. Currently, such screening programs require manual reading. False-positive errors in the reading process unnecessarily leads to costly follow-up and patient anxiety. Automated methods promise to provide more efficient, consistent and effective reading. To facilitate their development, a number of datasets have been created. Such datasets can aid in learning-based development but many are not publicly available and do not draw directly from population screening programs. With the aim of specifically targeting population screening programs, we introduce NL-Breast-Screening, a dataset from a Canadian provincial screening program. The dataset consists of 5997 mammography exams, each of which has four standard views and is biopsy-confirmed. Cases where radiologists’ reading was a false-positive are identified. NL-Breast-Screening is made publicly available as a new resource to promote advances in automation for population screening programs.</p>

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Full Field Digital Mammography Dataset from a Population Screening Program

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月25日

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  • Edward Kendall,
  • Parham Hajishafiezahramini,
  • Matthew Hamilton,
  • Gregory Doyle,
  • Nancy Wadden,
  • Oscar Meruvia-Pastor

摘要

Breast cancer presents the second largest cancer risk in the world to women. Early detection of cancer has been shown to be effective in reducing mortality. Population screening programs schedule regular mammography imaging for participants, promoting early detection. Currently, such screening programs require manual reading. False-positive errors in the reading process unnecessarily leads to costly follow-up and patient anxiety. Automated methods promise to provide more efficient, consistent and effective reading. To facilitate their development, a number of datasets have been created. Such datasets can aid in learning-based development but many are not publicly available and do not draw directly from population screening programs. With the aim of specifically targeting population screening programs, we introduce NL-Breast-Screening, a dataset from a Canadian provincial screening program. The dataset consists of 5997 mammography exams, each of which has four standard views and is biopsy-confirmed. Cases where radiologists’ reading was a false-positive are identified. NL-Breast-Screening is made publicly available as a new resource to promote advances in automation for population screening programs.