54 citations · 171 across the 12 of their papers we have counts for
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cs.CV2023★ 3 cited
M&M: Tackling False Positives in Mammography with a Multi-view and Multi-instance Learning Sparse Detector
Yen Nhi Truong Vu, Dan Guo, Ahmed Taha +2
Deep-learning-based object detection methods show promise for improving screening mammography, but high rates of false positives can hinder their effectiveness in clinical practice…
cs.CV2023★ 1 cited
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…