Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening

• Published in: IEEE transactions on medical imaging Vol. 39; no. 4; pp. 1184 - 1194
• Main Authors: Wu, Nan, Phang, Jason, Park, Jungkyu, Shen, Yiqiu, Huang, Zhe, Zorin, Masha, Jastrzebski, Stanislaw, Fevry, 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: Journal Article
• Language: English
• Published: United States IEEE 01.04.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Biomedical imaging; Breast - diagnostic imaging; Breast cancer; breast cancer screening; Breast Neoplasms - diagnostic imaging; Cancer; Cancer screening; Classification; deep convolutional neural networks; Deep Learning; Early Detection of Cancer - methods; Female; Humans; Image classification; Image Interpretation, Computer-Assisted - methods; Image resolution; Labels; Machine learning; Malignancy; Mammography; Mammography - methods; Medical imaging; Medical screening; Neural networks; Predictions; Predictive models; Radiologists; Subpopulations; Task analysis; Training

We present a deep convolutional neural network for breast cancer screening exam classification, trained, and evaluated on over 200000 exams (over 1000000 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 attr...