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

Authors:William Lotter,Abdul Rahman Diab, Bryan HaslamJiye G. Kim,Giorgia Grisot, Eric Wu,Kevin Wu,Jorge Onieva Onieva,Jerrold L. Boxerman, Meiyun Wang,Mack Bandler,Gopal Vijayaraghavan,A. Gregory Sorensen**************************     

                                   Submitted on (Dec) ******************************************** (v1) ), last revised (Dec) ******************************************** (this version, v2))    Abstract:Breast cancer remains a global challenge, causing over 1 million deaths globally in . 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 26 – (******************************************************%. 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 (*************************************************************%. Our results demonstrate promise towards software that can improve the accuracy of and access to screening mammography worldwide.            

      Submission history

From: William Lotter [view email]       
[v1]

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