Lessons from the first DBTex Challenge

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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Bibliographic Details
Published inNature machine intelligence Vol. 3; no. 8; pp. 735 - 736
Main Authors Park, Jungkyu, Shoshan, Yoel, Martí, Robert, Gómez del Campo, Pablo, Ratner, Vadim, Khapun, Daniel, Zlotnick, Aviad, Barkan, Ella, Gilboa-Solomon, Flora, Chłędowski, Jakub, Witowski, Jan, Millet, Alexandra, Kim, Eric, Lewin, Alana, Pysarenko, Kristine, Chen, Sardius, Goldberg, Julia, Patel, Shalin, Plaunova, Anastasia, Wegener, Melanie, Wolfson, Stacey, Lee, Jiyon, Hava, Sana, Murthy, Sindhoora, Du, Linda, Gaddam, Sushma, Parikh, Ujas, Heacock, Laura, Moy, Linda, Reig, Beatriu, Rosen-Zvi, Michal, Geras, Krzysztof J.
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 01.08.2021
Nature Publishing Group
Subjects
631/67/1347
631/67/2321
639/705/117
Breast cancer
Challenge Accepted
Datasets
Deep learning
Engineering
Mammography
Medical screening
Teams
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Summary: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.
ISSN:2522-5839
2522-5839
DOI:10.1038/s42256-021-00378-z