Breast cancer remains a global challenge, causing over 1 million deaths
globally in 2018. To achieve earlier breast cancer detection, screening x-ray
mammography is recommended by health organizations worldwide and has been
estimated to decrease breast cancer mortality by 20-40%. Nevertheless,
significant false positive and false negative rates, as well as high
interpretation costs, leave opportunities for improving quality and access. To
address these limitations, there has been much recent interest in applying deep
learning to mammography; however, obtaining large amounts of annotated data
poses a challenge for training deep learning models for this purpose, as does
ensuring generalization beyond the populations represented in the training
dataset. Here, we present an annotation-efficient deep learning approach that
1) achieves state-of-the-art performance in mammogram classification, 2)
successfully extends to digital breast tomosynthesis (DBT; "3D mammography"),
3) detects cancers in clinically-negative prior mammograms of cancer patients,
4) generalizes well to a population with low screening rates, and 5)
outperforms five-out-of-five full-time breast imaging specialists by improving
absolute sensitivity by an average of 14%. Our results demonstrate promise
towards software that can improve the accuracy of and access to screening
mammography worldwide.
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