Asset Details
Do you wish to reserve the book?
Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach
Do you wish to request the book?
Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach
2021
Overview
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
–
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.
A generalizable and interpretable artificial-intelligence system achieves clinical accuracy for screening and early breast-cancer detection on 2D and 3D mammograms.
Publisher
Nature Publishing Group US,Nature Publishing Group
