7 citations · 14 across the 5 of their papers we have counts for
5 papers
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…
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…
Cycle Text-To-Image GAN with BERT
Trevor Tsue, Samir Sen, Jason Li
We explore novel approaches to the task of image generation from their respective captions, building on state-of-the-art GAN architectures. Particularly, we baseline our models wit…
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…