小柯机器人

近日，美国DeepHealth 公司A. Gregory Sorensen、William Lotter等研究人员合作发现，深度学习方法可通过乳房摄影术实现可靠的乳腺癌检测。这一研究成果于2021年1月11日在线发表在国际学术期刊《自然—医学》上。

研究人员提供了一种注释有效的深度学习方法，该方法（1）在乳房X线照片分类中达到了最先进的性能，（2）成功地扩展到了数字乳房断层合成（DBT；'3D乳房X线照片'），（3）在临床乳房X线照片阴性结果中检测到癌症，（4）普遍适用于筛查率低的人群，（5）优于专门的乳房成像专家，平均敏感性提高14％。通过从DBT数据创建新的“最大怀疑投影”（MSP）图像，研究人员经过逐步训练的多实例学习方法仅使用乳房级别的标签有效地训练了DBT考试，同时保持了本地的可解释性。

总而言之，这些结果表明，该软件可以提高全球X线钼靶筛查的准确性并提高其可获取性。

据介绍，乳腺癌仍然是全球性挑战，2018年导致了60万多人死亡。为了实现早期癌症检测，全世界的卫生组织都建议进行乳房X线检查。据估计，乳房X线检查可以将乳腺癌的死亡率降低20％至40％。尽管乳腺钼靶筛查具有明确的价值，但显著的假阳性和假阴性率以及专业人员的不均匀性仍为改善质量和获取机会提供了机会。为了解决这些局限性，近来人们对将深度学习应用于乳腺X线照相术产生了浓厚的兴趣，这些努力突出了两个关键难题：获取大量带注释的训练数据，以及确保不同人群、购置设备和方式都适用。

附：英文原文

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

Author: William Lotter, Abdul Rahman Diab, Bryan Haslam, Jiye G. Kim, Giorgia Grisot, Eric Wu, Kevin Wu, Jorge Onieva Onieva, Yun Boyer, Jerrold L. Boxerman, Meiyun Wang, Mack Bandler, Gopal R. Vijayaraghavan, A. Gregory Sorensen

Issue&Volume: 2021-01-11

Abstract: 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 access4,5. To address these limitations, there has been much recent interest in applying deep learning to mammography6,7,8,9,10,11,12,13,14,15,16,17,18, 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.

DOI: 10.1038/s41591-020-01174-9

Source: https://www.nature.com/articles/s41591-020-01174-9

Nature Medicine：《自然—医学》，创刊于1995年。隶属于施普林格·自然出版集团，最新IF：87.241
官方网址：https://www.nature.com/nm/
投稿链接：https://mts-nmed.nature.com/cgi-bin/main.plex