4 citations · 10 across the 6 of their papers we have counts for
5 papers · 1 filter
Problems and shortcuts in deep learning for screening mammography
Trevor Tsue, Brent Mombourquette, Ahmed Taha +3
This work reveals undiscovered challenges in the performance and generalizability of deep learning models. We (1) identify spurious shortcuts and evaluation issues that can inflate…
Deep is a Luxury We Don't Have
Ahmed Taha, Yen Nhi Truong Vu, Brent Mombourquette +3
Medical images come in high resolutions. A high resolution is vital for finding malignant tissues at an early stage. Yet, this resolution presents a challenge in terms of modeling…
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…