4 citations · 6 across the 3 of their papers we have counts for
4 papers
A deep learning algorithm for reducing false positives in screening mammography
Stefano Pedemonte, Trevor Tsue, Brent Mombourquette +10
Screening mammography improves breast cancer outcomes by enabling early detection and treatment. However, false positive callbacks for additional imaging from screening exams cause…
A Hypersensitive Breast Cancer Detector
Stefano Pedemonte, Brent Mombourquette, Alexis Goh +6
Early detection of breast cancer through screening mammography yields a 20-35% increase in survival rate; however, there are not enough radiologists to serve the growing population…
Adaptation of a deep learning malignancy model from full-field digital mammography to digital breast tomosynthesis
Sadanand Singh, Thomas Paul Matthews, Meet Shah +6
Mammography-based screening has helped reduce the breast cancer mortality rate, but has also been associated with potential harms due to low specificity, leading to unnecessary exa…
A Multi-site Study of a Breast Density Deep Learning Model for Full-field Digital Mammography Images and Synthetic Mammography Images
Thomas P. Matthews, Sadanand Singh, Brent Mombourquette +12
Purpose: To develop a Breast Imaging Reporting and Data System (BI-RADS) breast density deep learning (DL) model in a multi-site setting for synthetic two-dimensional mammography (…