Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approach
This work addresses the challenge of improving breast cancer detection accuracy and access in screening mammography, with potential for broad clinical impact, though it builds on existing deep learning methods.
The paper tackled the problem of high false positive and negative rates in breast cancer screening by developing an annotation-efficient deep learning approach, which achieved state-of-the-art performance in mammogram classification, outperformed five specialists by improving sensitivity by an average of 14%, and generalized well to diverse populations.
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.