High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks
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Recent advances in deep learning for natural images has prompted a surge of interest in applying similar techniques to medical images. Most 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. This difference makes it inadequate to use the existing network architectures developed for natural images, because they work on an heavily downsampled image 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 thousand images. We focus on investigating the impact of 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. This suggests that medical imaging research using deep learning must utilize as much data as possible with the least amount of potentially harmful preprocessing.
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