A new international competition aims to speed up the development of AI models that can assist radiologists in detecting suspicious lesions from hundreds of millions of pixels in 3D mammograms. The top three winning teams compare notes.
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References
Aase, H. S. et al. Eur. Radiol. 29, 1175–1186 (2019).
Buda, M. et al. Preprint at https://arxiv.org/abs/2011.07995 (2020).
Clark, K. et al. J. Digit. Imaging 26, 1045–1057 (2013).
Buda, M. et al. Data from the breast cancer screening — digital breast tomosynthesis (BCS-DBT). The Cancer Imaging Archive https://go.nature.com/2T3Y7bg (2020).
Grand Challenge: Digital Breast Tomosynthesis Lesion Detection (SPIE, 2021); https://go.nature.com/3eklWDd
Shoshan, Y. et al. In Medical Image Computing and Computer Assisted Intervention (eds de Bruijne, M. et al.) (Springer, 2021).
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Park, J., Shoshan, Y., Martí, R. et al. Lessons from the first DBTex Challenge. Nat Mach Intell 3, 735–736 (2021). https://doi.org/10.1038/s42256-021-00378-z
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DOI: https://doi.org/10.1038/s42256-021-00378-z
