Design or audit the data-preparation stage of a medical-imaging model — DICOM/NIfTI intake, resampling and intensity normalisation, and the augmentation plan — so the pipeline is leakage-safe before model-scaffold builds the training repo. Emits a declarative preprocessing manifest and a deterministic data-stage leakage gate that catches the leaks a split table cannot see: a dataset-level normaliser fit on non-train data, any data-fitted transform run before the split, and the same patient's slices crossing splits. Integrates MONAI / TorchIO transforms; it does not reimplement them, and it never runs preprocessing on real patient data.
Design or audit the data-preparation stage of a medical-imaging model — DICOM/NIfTI intake, resampling and intensity normalisation, and the augmentation plan — so the pipeline is leakage-safe before model-scaffold builds the training repo. Emits a declarative preprocessing manifest and a deterministic data-stage leakage gate that catches the leaks a split table cannot see: a dataset-level normaliser fit on non-train data, any data-fitted transform run before the split, and the same patient's slices crossing splits. Integrates MONAI / TorchIO transforms; it does not reimplement them, and it never runs preprocessing on real patient data.
triggers
preprocess imaging, preprocessing, data pipeline, DICOM, NIfTI, resample, spacing, intensity normalization, intensity normalisation, windowing, HU window, z-score, histogram matching, augmentation, augmentation plan, TorchIO, MONAI transforms, data leakage, normalization leakage, preprocessing manifest, fit on train, per-image normalization, patient-level split, slice-level leakage, imaging data prep
tools
Read, Write, Edit, Bash, Grep, Glob
model
inherit
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 . 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, 2023;
Varoquaux & Cheplygina, 2022; CLAIM 2024 data items).
leakage-safe by construction
Patterns
npj Digit Med
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.
When to use
You have a data manifest (one row per image/slice with a patient/subject ID) and want a
leakage-safe preprocessing plan + a machine-checkable manifest before scaffolding a model.
You want to audit an existing preprocessing pipeline for data-stage leakage.
When NOT to use
Auditing the train/val/test split table itself → /model-validation (split-leakage gate).
Building the training repo / model code → /model-scaffold (it consumes this manifest).
Choosing the architecture → /architecture-zoo.
Held-out metrics / calibration → /model-evaluation then /analyze-stats.
Reimplementing MONAI / TorchIO transforms → out of scope (this skill wires and audits them).
Workflow
Phase 1 — Inventory the data and the intended steps
Collect: modality (CT / MR / X-ray / US / path), the data manifest (one row per image/slice with a
patient_id), the intended resample spacing, the intensity transform (fixed HU window vs a fitted
z-score / min-max / histogram match), and the augmentation plan. See
references/preprocessing_guide.md for modality-aware guidance
(what normalisation is standard per modality, which augmentations preserve vs break physiology).
Phase 2 — Decide fit scope and order (the leakage-safe rules)
Fit dataset-level normalisation on the training split only — never on all/full/test.
Run any data-fitted transform AFTER the split — before the split there is no train/test
distinction, so the fit spans partitions.
Prefer per-image (per-sample) normalisation where clinically appropriate: it uses only that
image's own statistics and is leakage-free even before the split.
Keep augmentation train-only — augmenting val/test folds undisclosed test-time augmentation
into the reported metric.
Split at the patient level, then map slices to their patient's split (never split slices).
Phase 3 — Emit the preprocessing manifest
Write a declarative JSON manifest that model-scaffold consumes and the gate checks:
Declare the fit scope of resampling too. A target spacing you chose in advance is fixed and
never leaks (fit_scope: fixed). A target derived from the cohort does: nnU-Net sets its target
spacing from a percentile of the dataset fingerprint, so a resample fitted over every case carries
held-out geometry into the training grid exactly as an intensity statistic would. Which one you
have is decided by the fingerprint's scope, not by the word "resample".
That gate asks whether a transform was fit on the right scope. Before an inference run on a
cohort the model was not trained on, ask the other question — is that cohort in the intensity
domain the trained normaliser assumes?
Verdicts: PREPROCESS_BEFORE_SPLIT, NORMALIZATION_LEAKAGE, PATIENT_CROSS_SPLIT (Major);
AUGMENTATION_ON_EVAL, UNSPECIFIED_FIT_SCOPE, MISSING_SEED (Minor). The verdict is reproduced
by set arithmetic + rule on the manifest, never asserted from prose. A green gate is a precondition
for handing the manifest to /model-scaffold.
Integration
Feeds /model-scaffold — the audited manifest is the scaffold's preprocessing input; its
split_assignment is the same patient-level split /model-validation later re-verifies.
/self-reviewmodel_development probe audits data-stage leakage in a finished manuscript;
this skill produces the leakage-safe pipeline it looks for.
/check-reporting — the manifest documents the CLAIM 2024 / TRIPOD+AI data-preprocessing items.
Anti-Hallucination
Never fabricate image statistics, patient IDs, or split assignments. Every value in the
manifest comes from the real data manifest and the researcher's declared pipeline — never invented.
This skill designs and audits the plan; it does not run preprocessing on real patient data or
synthesise the images it describes.
Never report a preprocessing-audit "pass" without running check_preprocessing_leakage.py. The
leakage verdict is reproduced deterministically (rule + set arithmetic on the manifest), never
asserted from prose.
Never label a dataset-fitted transform as per-sample to clear the gate. The manifest's
type / fit_scope / stage must describe what the code actually does; a mislabelled transform
hides a real leak the gate would otherwise catch.
Integrate, don't reimplement. Reference MONAI / TorchIO transforms; do not write a new
normalisation/resampling implementation or claim results for one.
Reproducible challenge
scripts/check_normalizer_domain_challenge/ ships a synthetic profile/contract triple: a cohort in
the contract's own domain that must come back clean (the false-positive guard), an arbitrary-unit
cohort that must raise a Major, and an unreadable contract that must refuse rather than pass.
scripts/check_preprocessing_leakage_challenge/ ships a synthetic leak/clean manifest pair with a
network-free verify.sh wired into the skill's validation commands.
