uncertainty-imaging

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.