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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Detalles Bibliográficos
Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7427471/
https://www.ncbi.nlm.nih.gov/pubmed/31603772
http://dx.doi.org/10.1109/TMI.2019.2945514

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7427471/
https://www.ncbi.nlm.nih.gov/pubmed/31603772
http://dx.doi.org/10.1109/TMI.2019.2945514

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