Breast cancer remains a global challenge, causing over 600,000 deaths in 2018 (ref. ¹). To achieve earlier cancer detection, health organizations worldwide recommend screening mammography, which is estimated to decrease breast cancer mortality by 20–40% (refs. ^2,3). Despite the clear value of screening mammography, significant false positive and false negative rates along with non-uniformities in expert reader availability leave opportunities for improving quality and access^4,5. To address these limitations, there has been much recent interest in applying deep learning to mammography^{6,7,8,9,10,11,12,13,14,15,16,17,18}, and these efforts have highlighted two key difficulties: obtaining large amounts of annotated training data and ensuring generalization across populations, acquisition equipment and modalities. 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 patients with cancer, (4) generalizes well to a population with low screening rates and (5) outperforms five out of five full-time breast-imaging specialists with an average increase in sensitivity of 14%. By creating new ‘maximum suspicion projection’ (MSP) images from DBT data, our progressively trained, multiple-instance learning approach effectively trains on DBT exams using only breast-level labels while maintaining localization-based interpretability. Altogether, our results demonstrate promise towards software that can improve the accuracy of and access to screening mammography worldwide.

乳腺癌仍然是一项全球性挑战，2018 年导致超过 600,000 人死亡（参考文献¹）。为了实现早期癌症检测，世界各地的卫生组织建议筛查乳房 X 光检查，据估计可将乳腺癌死亡率降低 20-40%（参考文献^2,3）。尽管筛查乳房 X 光检查具有明确的价值，但显着的假阳性和假阴性率以及专家读者可用性的不均匀性为提高质量和访问^4,5留下了机会。为了解决这些限制，最近人们对将深度学习应用于乳房 X 线摄影^{6、7、8、9、10、11、12、13、14、15、16、17、18产生了浓厚的兴趣}，这些努力突出了两个关键困难：获取大量带注释的训练数据，并确保跨人群、采集设备和模式的泛化。在这里，我们提出了一种注释高效的深度学习方法，该方法 (1) 在乳房 X 线照片分类中实现了最先进的性能，(2) 成功扩展到数字乳房断层合成 (DBT；“3D 乳房 X 线摄影”)，(3) 检测癌症在癌症患者的临床阴性先前乳房X线照片中，（4）很好地推广到筛查率低的人群，并且（5）优于五分之五的全职乳房成像专家，灵敏度平均增加14％。通过从 DBT 数据中创建新的“最大怀疑投影”（MSP）图像，我们经过逐步训练，多实例学习方法仅使用乳房级标签有效地训练 DBT 考试，同时保持基于本地化的可解释性。总而言之，我们的结果证明了对可以提高全球范围内筛查乳房 X 线照相术的准确性和可及性的软件的前景。