arXiv cs.AI· Zhengshuai Yang, Linna Jiang, Changyun Li, Guihu Zhao·· 12 小时前AI 评分31
风险控制只认证错误率不认证错误幅度:选择性胎儿生物测量的审计
Risk Control Certifies Rates, Not Magnitudes: An Audit of Selective Fetal Biometry
AI 导读
对基于工具的胎儿头围生物测量流程审计发现,Clopper-Pearson 风险控制给出的认证与手工设定阈值无法区分,但流程仍输出 271.3 mm 的误差(真实 HC 为 324.6 mm),该候选完整度得分 0.94,约 150 个答案中的一个离群值不影响 α = 0.10 的认证风险界。
正文 · 原文
Abstract:Prenatal ultrasound biometry must return a head circumference (HC) and refuse when the image cannot support one. Selective prediction trades coverage against error, but its finite-sample guarantees bound the rate of gross errors, not their magnitude. We audit a tool-grounded biometry pipeline on two separately pre-registered four-way splits of the 999 labelled HC18 images, retraining the segmenter three times on each. Calibrating the contour-completeness threshold by exact Clopper-Pearson risk control returns a certificate indistinguishable from a hand-set floor, yet the pipeline still emits a 271.3 mm error: a small, geometrically complete ring fitted to an image whose true HC is 324.6 mm. The accepted candidate scores 0.94 completeness, so the score cannot see the failure, and one outlier among $\approx 150$ answers leaves the certified risk bound ($\alpha = 0.10$) intact. A second, untouched split reproduces the mechanism on a different image (233.3 mm). Constraining the admissibility region---the anatomical range in which the measurand is defined---removes the family instead: raising the lower bound from 50 to 55 mm costs 0.4 coverage points and cuts the worst-case error on both splits to 11.4 and 11.1 mm, whereas suppressing it through the completeness threshold costs 19.0 and 12.1 points. At an anatomically defined 70 mm the frozen tests give 1.91 and 1.81 mm MAE.
| Comments: | 10 pages, 2 figures, 3 tables |
| Subjects: | Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2611.05851 [cs.AI] |
| (or arXiv:2611.05851v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.05851 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zhengshuai Yang [view email]
[v1]
Wed, 4 Nov 2026 02:41:56 UTC (173 KB)
来源:arXiv cs.AI · arxiv.org