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TRUST: Threshold-Recalibrated Uncertainty-Safe Training for Certified Dismissal in Breast Cancer Screening

arXiv 2609.00300 ↗First posted 31 Aug 2026cs.CV cs.LGcited by 0

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Abstract (arXiv)

Reducing the review of clearly cancer-negative screening mammograms could lower radiologist workload without compromising cancer detection. We propose a closed-loop threshold-aware training strategy in which the dismissal threshold is recalculated during training and used to penalize cancer-positive images that approach the dismissal region. We evaluated the method on NLBS and RSNA using five controlled training configurations, with case-level assessment based on a one-sided 99% Clopper--Pearson upper bound for cancer prevalence among dismissed cases. The proposed model achieved the highest case-level dismissal rates at both 98% and 95% recall targets. On NLBS, dismissal reached 19.74% and 21.70%, while the cross-entropy baseline did not meet either recall target. On RSNA, dismissal improved from 7.04% to 14.31% and from 13.49% to 19.69%. In external RSNA→NLBS evaluation, the proposed model achieved dismissal rates of 12.95% and 19.87% at the 98% and 95% recall targets, respectively. These results support closed-loop threshold-aware training for high-recall selective dismissal.

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16 of 40 references.

References · 16

  1. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  2. 2017Selective Classification for Deep Neural Networkscited by 852
  3. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 792
  4. 2018Confounding variables can degrade generalization performance of radiological deep learning modelscited by 379
  5. 2020Calibrating Deep Neural Networks using Focal Losscited by 357
  6. 2019SelectiveNet: A Deep Neural Network with an Integrated Reject Optioncited by 350
  7. 2022Conformal Risk Controlcited by 299
  8. 2021Distribution-Free, Risk-Controlling Prediction Setscited by 288
  9. 2019Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screeningcited by 95
  10. 2024Mammo-CLIP: A Vision Language Foundation Model to Enhance Data Efficiency and Robustness in Mammographycited by 38
  11. 2025Conformal Risk Training: End-to-End Optimization of Conformal Risk Controlcited by 11
  12. 2020Automatic segmentation with detection of local segmentation failures in cardiac MRIcited by 10
4 more
  1. 2022Domain generalization in deep learning-based mass detection in mammography: A large-scale multi-center studycited by 10
  2. 2023Understanding Calibration of Deep Neural Networks for Medical Image Classificationcited by 5
  3. 2024Full Field Digital Mammography Dataset from a Population Screening Programcited by 4
  4. 2026Dataset-Origin Signatures and Shortcut Learning in Screening Mammography AI: A Cross-Dataset Case Studycited by 1

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