Uncertainty-Imaging Skill

Purpose

A medical-imaging model framed for deployment must say more than "class 1, 0.87". It needs a calibrated uncertainty on each case, an out-of-distribution (OOD) guard validated on data known to be out-of-distribution, and — if it abstains — a pre-specified operating point. The failures are predictable and reviewer-visible: a clinical-use claim built on point predictions, conformal intervals quoted without ever measuring their coverage, an "OOD detector" evaluated only on in-distribution data, a deep ensemble whose members share a seed, and uncertainty validated only in-distribution when deployment sees scanner/site/case-mix shift. This skill designs that layer and audits an existing one (Gal 2016; Lakshminarayanan 2017; Angelopoulos & Bates; Ovadia 2019; DECIDE-AI).

It is the deployment-safety companion in the model-engineering lane: /model-evaluation computes the held-out metrics and calibration, and uncertainty-imaging covers the uncertainty / OOD / abstention machinery a deployment claim rests on. It integrates MAPIE (conformal), captum, and pretrained OOD scorers; it does not reimplement them and never runs a model on real patient data.