3 papers
cs.CV2026
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…
cs.CV2026
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…
cs.CV2024
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…