Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening

We present a deep convolutional neural network for breast cancer screening exam classification, trained, and evaluated on over 200 000 exams (over 1 000 000 images). Our network achieves an AUC of 0.895 in predicting the presence of cancer in the breast, when tested on the screening population. We a...

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Bibliografiske detaljer
Udgivet i:IEEE Trans Med Imaging
Main Authors: Wu, Nan, Phang, Jason, Park, Jungkyu, Shen, Yiqiu, Huang, Zhe, Zorin, Masha, Jastrzębski, Stanisław, Févry, Thibault, Katsnelson, Joe, Kim, Eric, Wolfson, Stacey, Parikh, Ujas, Gaddam, Sushma, Lin, Leng Leng Young, Ho, Kara, Weinstein, Joshua D., Reig, Beatriu, Gao, Yiming, Toth, Hildegard, Pysarenko, Kristine, Lewin, Alana, Lee, Jiyon, Airola, Krystal, Mema, Eralda, Chung, Stephanie, Hwang, Esther, Samreen, Naziya, Kim, S. Gene, Heacock, Laura, Moy, Linda, Cho, Kyunghyun, Geras, Krzysztof J.
Format: Artigo
Sprog:Inglês
Udgivet: 2019
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Article
Online adgang:https://ncbi.nlm.nih.gov/pmc/articles/PMC7427471/
https://ncbi.nlm.nih.gov/pubmed/31603772
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/TMI.2019.2945514
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