Convolutional neural networks for breast cancer detection in mammography: A survey
Despite its proven record as a breast cancer screening tool, mammography remains labor-
intensive and has recognized limitations, including low sensitivity in women with dense …
intensive and has recognized limitations, including low sensitivity in women with dense …
Digital breast tomosynthesis: concepts and clinical practice
A Chong, SP Weinstein, ES McDonald, EF Conant - Radiology, 2019 - pubs.rsna.org
Digital breast tomosynthesis (DBT) is emerging as the standard of care for breast imaging
based on improvements in both screening and diagnostic imaging outcomes. The additional …
based on improvements in both screening and diagnostic imaging outcomes. The additional …
[HTML][HTML] Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams
Though consistently shown to detect mammographically occult cancers, breast ultrasound
has been noted to have high false-positive rates. In this work, we present an AI system that …
has been noted to have high false-positive rates. In this work, we present an AI system that …
Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach
Breast cancer remains a global challenge, causing over 600,000 deaths in 2018 (ref.). To
achieve earlier cancer detection, health organizations worldwide recommend screening …
achieve earlier cancer detection, health organizations worldwide recommend screening …
Diagnostic accuracy of digital screening mammography with and without computer-aided detection
Importance After the US Food and Drug Administration (FDA) approved computer-aided
detection (CAD) for mammography in 1998, and the Centers for Medicare and Medicaid …
detection (CAD) for mammography in 1998, and the Centers for Medicare and Medicaid …
Deep learning in mammography: diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer
Objectives The aim of this study was to evaluate the diagnostic accuracy of a multipurpose
image analysis software based on deep learning with artificial neural networks for the …
image analysis software based on deep learning with artificial neural networks for the …
A deep learning method for classifying mammographic breast density categories
AA Mohamed, WA Berg, H Peng, Y Luo… - Medical …, 2018 - Wiley Online Library
Purpose Mammographic breast density is an established risk marker for breast cancer and
is visually assessed by radiologists in routine mammogram image reading, using four …
is visually assessed by radiologists in routine mammogram image reading, using four …
[HTML][HTML] Deep learning vs. radiomics for predicting axillary lymph node metastasis of breast cancer using ultrasound images: don't forget the peritumoral region
Objective: Axillary lymph node (ALN) metastasis status is important in guiding treatment in
breast cancer. The aims were to assess how deep convolutional neural network (CNN) …
breast cancer. The aims were to assess how deep convolutional neural network (CNN) …
Improving accuracy and efficiency with concurrent use of artificial intelligence for digital breast tomosynthesis
EF Conant, AY Toledano, S Periaswamy… - Radiology: Artificial …, 2019 - pubs.rsna.org
Purpose To evaluate the use of artificial intelligence (AI) to shorten digital breast
tomosynthesis (DBT) reading time while maintaining or improving accuracy. Materials and …
tomosynthesis (DBT) reading time while maintaining or improving accuracy. Materials and …
Ultrasound as the primary screening test for breast cancer: analysis from ACRIN 6666
WA Berg, AI Bandos, EB Mendelson… - Journal of the …, 2016 - academic.oup.com
Background: Mammography is not widely available in all countries, and breast cancer
incidence is increasing. We considered performance characteristics using ultrasound (US) …
incidence is increasing. We considered performance characteristics using ultrasound (US) …