Calibrating on Medical Segmentation Model through Signed Distance
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(Published version)
Date
2025
Authors
Liang, W.
Zhang, W.E.
Yue, L.
Xu, M.
Maennel, O.
Chen, W.
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Conference paper
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Proceedings of the 34th ACM International Conference on Information and Knowledge Management, 2025, pp.1778-1787
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Wenhao Liang, Wei Emma Zhang, Lin Yue, Miao Xu, Olaf Maennel and Weitong Chen
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ACM International Conference on Information and Knowledge Management (CIKM) (10 Nov 2025 - 14 Nov 2025 : Seoul)
Abstract
Classical overlap metrics such as Dice or IoU quantify where a medical-image segmentation falls short but say nothing about the confidence of each prediction. Over-confident errors are particularly dangerous in clinical practice, where a single false-positive voxel may trigger an unnecessary biopsy. We introduce three contributions that jointly address spatial precision and reliability. (i) Signed-Distance Calibration (SDC) loss couples cross-entropy, local calibration and a differentiable signed-distance penalty, enforcing boundary accuracy while moderating confidence. (ii) A Spatially Adaptive Margin (SAM) module applies lightweight morphological transforms to ground-truth masks before computing the local target, sharpening ambiguous edges. (iii) Pixel-wise Expected Calibration Error (pECE) extends ECE to millions of voxels and penalises high-confidence false positives. Across four public datasets (ACDC, FLARE, BraTS, PROSTATE) and two back-bones (U-Net, nnU-Net), SDC improves Dice by up to 4 percentage points and halves ECE compared with the state of the art, without sacrificing runtime.
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© 2025 Copyright held by the owner/author(s). This is an open access article