Title: Deep Learning to Improve Breast Cancer Early Detection on Screening Mammography,
Abstract: The rapid development of deep learning, a family of machine learning
techniques, has spurred much interest in its application to medical imaging
problems. Here, we develop a deep learning algorithm that can accurately detect
breast cancer on screening mammograms using an "end-to-end" training approach
that efficiently leverages training datasets with either complete clinical
annotation or only the cancer status (label) of the whole image. In this
approach, lesion annotations are required only in the initial training stage,
and subsequent stages require only image-level labels, eliminating the reliance
on rarely available lesion annotations. Our all convolutional network method
for classifying screening mammograms attained excellent performance in
comparison with previous methods. On an independent test set of digitized film
mammograms from Digital Database for Screening Mammography (DDSM), the best
single model achieved a per-image AUC of 0.88, and four-model averaging
improved the AUC to 0.91 (sensitivity: 86.1%, specificity: 80.1%). On a
validation set of full-field digital mammography (FFDM) images from the
INbreast database, the best single model achieved a per-image AUC of 0.95, and
four-model averaging improved the AUC to 0.98 (sensitivity: 86.7%, specificity:
96.1%). We also demonstrate that a whole image classifier trained using our
end-to-end approach on the DDSM digitized film mammograms can be transferred to
INbreast FFDM images using only a subset of the INbreast data for fine-tuning
and without further reliance on the availability of lesion annotations. These
findings show that automatic deep learning methods can be readily trained to
attain high accuracy on heterogeneous mammography platforms, and hold
tremendous promise for improving clinical tools to reduce false positive and
false negative screening mammography results.