Lessons from the first DBTex Challenge

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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Bibliographische Detailangaben
Veröffentlicht in:Nature machine intelligence 2021-08, Vol.3 (8), p.735-736
Hauptverfasser: 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: Artikel
Sprache:eng
Schlagworte:
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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Beschreibung
Zusammenfassung: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