arXiv：cs.LG· Md Kawsher Mahbub, Milon Biswas, Mirza Niaz Morshed, Wei Yu·· 4 小时前AI 评分45
SpatialUQ：面向黑盒视觉模型的空间一致性事后不确定性量化
SpatialUQ: Post-Hoc Uncertainty Quantification from Spatial Consistency in Black-Box Vision Models
AI 导读
SpatialUQ 是一种仅用输出概率的事后不确定性方法，通过六次确定性前向计算中全局预测与五个固定空间裁剪均值的 Jensen-Shannon 散度衡量可信度。
正文
Abstract:Clinical vision models are often deployed as frozen black boxes with no access to internals, retraining, or ground truth at inference time. We introduce \textbf{SpatialUQ}, a post-hoc uncertainty method using only output probabilities. It measures the Jensen-Shannon divergence between the global prediction and the mean of five fixed spatial crops in six deterministic forward passes. The premise is simple, trustworthy predictions are spatially consistent. On NIH ChestX-ray14 (DenseNet-121, $N{=}25{,}596$), our Multicrop Uncertainty Score (MUS) reaches $0.784$ failure-detection AUC versus $0.664$ for MC-Dropout ($p{<}10^{-6}$) at one-fifth the compute, with native calibration ($\text{SCE}{=}0.049$ vs.\ $0.127$ for $\ell_1$), the best-calibrated among methods above 0.78 AUC. A supervised fusion of MUS with entropy, confidence, and $\ell_1$ reaches $0.832$, outperforming a five-member ensemble ($0.813$). MUS scales with model quality, reaching $0.899$ with BiomedCLIP ($\rho = 0.846$), while this relationship remains meaningful in-distribution ($\rho = 0.523$) but breaks down under severe distribution shift (VinBigData, $\rho = 0.027$). MUS is well-suited to diffuse findings but is less dependable for small focal lesions such as nodules. Code and experimental materials are publicly available at this https URL.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.09498 [cs.CV] |
| (or arXiv:2610.09498v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.09498 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Md Kawsher Mahbub [view email]
[v1]
Wed, 7 Oct 2026 05:53:08 UTC (7,765 KB)
来源：arXiv：cs.LG · arxiv.org