Bound-Aware Per-Organ Recall Risk Control for Multi-Organ CT Segmentation under Clinical Domain Shift
Read the original on arXiv AI →The paper presents a distribution‑free risk‑control framework that provides organ‑specific recall guarantees for frozen multi‑organ CT segmentation models. It calibrates per‑organ thresholds for an AMOS‑trained nnU‑Net, audits its transfer to RAOS, and estimates local re‑certification costs using case‑level voxel false‑negative rates. The study compares Risk‑Controlling Prediction Sets (RCPS) and Conformal Risk Control (CRC), noting that RCPS offers high‑probability control of population‑mean risk while CRC provides weaker expectation control, and evaluates the effectiveness of the Waudby–Smith–Ramdas betting bound versus Hoeffding–Bentkus bounds for re‑certification. "whyItMatters":"The work demonstrates how to maintain organ‑level recall guarantees when deploying segmentation models across different clinical domains, highlighting the trade‑offs between threshold conservatism and re‑certification effort."
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