Title: High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks,
Abstract: Advances in deep learning for natural images have prompted a surge of
interest in applying similar techniques to medical images. The majority of the
initial attempts focused on replacing the input of a deep convolutional neural
network with a medical image, which does not take into consideration the
fundamental differences between these two types of images. Specifically, fine
details are necessary for detection in medical images, unlike in natural images
where coarse structures matter most. This difference makes it inadequate to use
the existing network architectures developed for natural images, because they
work on heavily downscaled images to reduce the memory requirements. This hides
details necessary to make accurate predictions. Additionally, a single exam in
medical imaging often comes with a set of views which must be fused in order to
reach a correct conclusion. In our work, we propose to use a multi-view deep
convolutional neural network that handles a set of high-resolution medical
images. We evaluate it on large-scale mammography-based breast cancer screening
(BI-RADS prediction) using 886,000 images. We focus on investigating the impact
of the training set size and image size on the prediction accuracy. Our results
highlight that performance increases with the size of training set, and that
the best performance can only be achieved using the original resolution. In the
reader study, performed on a random subset of the test set, we confirmed the
efficacy of our model, which achieved performance comparable to a committee of
radiologists when presented with the same data.