uncertainty-imaging

uncertainty-imaging is a Computer vision skill for AI agents, published by Aperivue in the medsci-skills repository on GitHub. Skillspace rates uncertainty-imaging C (72/100) and it passed the safety scan; the rating is based on static analysis only.

Design or audit the uncertainty-quantification, out-of-distribution (OOD) detection, and selective-prediction layer of a medical-imaging model framed for deployment — so a clinical-use claim carries calibrated per-case uncertainty (MC-dropout / deep ensemble / conformal / Bayesian), an OOD guard validated on a held-out OOD set, an abstention rule at a pre-specified operating point, and uncertainty checked under distribution shift. Emits an uncertainty manifest and a deterministic gate that flags a deployment claim built on point predictions, conformal intervals with unmeasured coverage, and an OOD claim with no held-out OOD data. Integrates MAPIE / captum / pretrained OOD scorers; it does not reimplement them and never runs a model on real patient data.

Grade

C

Quality

72 / 100

Safety

Passed

GitHub stars

201

Unproven: rated by static analysis only, not yet compared head-to-head.

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