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 whether
there is a cancer in the breast, when tested on the screening population. We
attribute the high accuracy of our model to a two-stage training procedure,
which allows us to use a very high-capacity patch-level network to learn from
pixel-level labels alongside a network learning from macroscopic breast-level
labels. To validate our model, we conducted a reader study with 14 readers,
each reading 720 screening mammogram exams, and find our model to be as
accurate as experienced radiologists when presented with the same data.
Finally, we show that a hybrid model, averaging probability of malignancy
predicted by a radiologist with a prediction of our neural network, is more
accurate than either of the two separately. To better understand our results,
we conduct a thorough analysis of our network's performance on different
subpopulations of the screening population, model design, training procedure,
errors, and properties of its internal representations.

本文提出了一个基于深度卷积神经网络的乳腺癌筛查检查分类方法，并在超过 200,000 个检查（超过 1,000,000 张图像）上进行了训练和评估。该网络在筛查人群中预测乳腺癌的 AUC 为 0.895，其高准确度的原因是采用了两阶段训练程序，并通过可靠的读者研究验证了准确性。最后，研究发现由我们的神经网络预测恶性概率与放射科医生的预测平均值相结合会更加准确。