uncertainty-imaging

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.

When to use

• Your model is framed for clinical use / deployment and a reviewer will ask "what does it do when it is unsure, or off-distribution?"
• You report conformal / MC-dropout / ensemble uncertainty and want the coverage, independence, and shift checks right before submission.
• You want to audit an existing uncertainty/OOD section for the failure modes below.

When NOT to use

• Held-out discrimination / calibration metrics of the point predictor → /model-evaluation then /analyze-stats.
• Training-repo scaffolding / the split → /model-scaffold (+ /model-validation).
• Interpretability / saliency of a trained network → /explainability.
• Classical-ML calibration of a tabular model → /radiomics-ml + /analyze-stats.
• Reimplementing MAPIE / an OOD library → out of scope (this skill wires and audits them).