3 papers · 1 filter
TRUST: Threshold-Recalibrated Uncertainty-Safe Training for Certified Dismissal in Breast Cancer Screening
Parham Hajishafiezahramini, Matthew Hamilton, Edward Kendall +2
Reducing the review of clearly cancer-negative screening mammograms could lower radiologist workload without compromising cancer detection. We propose a closed-loop threshold-aware…
Dataset-Origin Signatures and Shortcut Learning in Screening Mammography AI: A Cross-Dataset Case Study
Parham Hajishafiezahramini, Matthew Hamilton, Oscar Meruvia-Pastor +1
Reliable AI for screening mammography requires training data representative of the low cancer prevalence and subtle abnormalities found in screening populations. We examined whethe…
Full Field Digital Mammography Dataset from a Population Screening Program
Edward Kendall, Paraham Hajishafiezahramini, Matthew Hamilton +3
Breast cancer presents the second largest cancer risk in the world to women. Early detection of cancer has been shown to be effective in reducing mortality. Population screening pr…