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arXiv Machine Learning By Parham Hajishafiezahramini, Matthew Hamilton, Edward Kendall, Gregory Doyle, Oscar Meruvia Pastor

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

Read the original on arXiv Machine Learning →

The paper introduces TRUST, a threshold‑recalibrated training method that dynamically adjusts the dismissal threshold during training to penalize cancer‑positive images near the dismissal region. Evaluated on NLBS and RSNA datasets, TRUST achieved higher case‑level dismissal rates while maintaining 98% and 95% recall, outperforming a cross‑entropy baseline. External validation on RSNA→NLBS data confirmed improved dismissal rates at both recall targets, demonstrating the effectiveness of closed‑loop threshold‑aware training for selective dismissal in breast cancer screening.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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