Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening

Understand

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.