Summary of Full Field Digital Mammography Dataset From a Population Screening Program, by Edward Kendall et al.
Full Field Digital Mammography Dataset from a Population Screening Program
by Edward Kendall, Paraham Hajishafiezahramini, Matthew Hamilton, Gregory Doyle, Nancy Wadden, Oscar Meruvia-Pastor
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces NL-Breast-Screening, a dataset of 5997 mammography exams from a Canadian provincial breast cancer screening program. The dataset aims to facilitate the development of automated methods for reading mammograms and reducing false-positive errors. It consists of four standard views per exam, each biopsy-confirmed, including cases where radiologist readings were false positives. This resource is publicly available to promote advances in automation for population screening programs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new dataset called NL-Breast-Screening that helps develop machines to read mammograms better and fewer mistakes are made. It has 5997 exams with four views each, all confirmed by biopsies, including some where doctors were wrong. This will help make cancer detection more efficient and reduce anxiety for patients. |
