Preprocess-Imaging Skill

Purpose

This skill designs and audits the data-preparation stage of a medical-imaging model — the stage before a training repo is built — and proves it is leakage-safe by construction. Data leakage enters one step earlier than the split table can see: a normaliser fit on the whole dataset, a data-fitted transform run before the split exists, or a patient whose slices land in more than one partition. Each silently inflates every downstream metric (Kapoor & Narayanan, Patterns 2023; Varoquaux & Cheplygina, npj Digit Med 2022; CLAIM 2024 data items).

It is the missing first link in the lane: preprocess-imaging (prepare + audit) → /model-scaffold (build) → /model-validation (validate the split) → /model-evaluation + /analyze-stats (metrics) → /write-paper + /check-reporting (publish). It integrates MONAI / TorchIO transforms (referenced in the emitted plan); it does not reimplement them, and it never executes preprocessing on real patient data.