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Breast cancer remains a global challenge, causing over 1 million deathsglobally in 2018. To achieve earlier breast cancer detection, screening x-raymammography is recommended by health organizations worldwide and has beenestimated to decrease breast cancer mortality by 20-40%. Nevertheless,significant false positive and false negative rates, as well as highinterpretation costs, leave opportunities for improving quality and access. Toaddress these limitations, there has been much recent interest in applying deeplearning to mammography; however, obtaining large amounts of annotated dataposes a challenge for training deep learning models for this purpose, as doesensuring generalization beyond the populations represented in the trainingdataset. Here, we present an annotation-efficient deep learning approach that1) 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 improvingabsolute sensitivity by an average of 14%. Our results demonstrate promisetowards software that can improve the accuracy of and access to screeningmammography worldwide.

Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approach