Full Field Digital Mammography Dataset from a Population Screening Program
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arXiv
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| Format: | Preprint |
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2024
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| author | Kendall, Edward Hajishafiezahramini, Paraham Hamilton, Matthew Doyle, Gregory Wadden, Nancy Meruvia-Pastor, Oscar |
| author_facet | Kendall, Edward Hajishafiezahramini, Paraham Hamilton, Matthew Doyle, Gregory Wadden, Nancy Meruvia-Pastor, Oscar |
| contents | 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. 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 radiologist reading was a false-positive are identified. NL-Breast is made publicly available as a new resource to promote advances in automation for population screening programs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_02710 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Full Field Digital Mammography Dataset from a Population Screening Program Kendall, Edward Hajishafiezahramini, Paraham Hamilton, Matthew Doyle, Gregory Wadden, Nancy Meruvia-Pastor, Oscar Computer Vision and Pattern Recognition Machine Learning 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. 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 radiologist reading was a false-positive are identified. NL-Breast is made publicly available as a new resource to promote advances in automation for population screening programs. |
| title | Full Field Digital Mammography Dataset from a Population Screening Program |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2411.02710 |