Abstract
Early diagnosis of breast cancer is highly dependent on quality breast imaging and precise image interpretation. The BREAST programme is an innovative strategy for reader performance self-evaluation in breast cancer detection. Using an online system, detailed feedback on reader/image interpretation is given instantly. Our strategy is currently focused on mammograms but has the potential to be available for a wide range of medical imaging modalities. BREAST also serves a solution to researchers requiring large observer numbers by facilitating the involvement of experts wherever they are located. In summary, BREAST improves the efficacy of mammographic cancer detection through a system of reader performance monitoring and enables research studies with a large amount of robust data. © 2014 Springer International Publishing.
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Brennan, P. C., Trieu, P. D., Tapia, K., Ryan, J., Mello-Thoms, C., & Lee, W. (2014). BREAST: A novel strategy to improve the detection of breast cancer. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8539 LNCS, pp. 438–443). Springer Verlag. https://doi.org/10.1007/978-3-319-07887-8_61
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