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.

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:

{
  "split_seed": 42,
  "transforms": [
    {"name": "hu_window", "type": "clip", "fit_scope": "none", "stage": "before_split"},
    {"name": "train_zscore", "type": "standardize", "fit_scope": "train", "stage": "after_split"},
    {"name": "flip_rotate", "type": "augmentation", "stage": "after_split", "applies_to": ["train"]}
  ],
  "split_assignment": [
    {"patient_id": "P001", "unit_id": "P001_s1", "split": "train"}
  ]
}

fit_scope: train (OK) · all/full/dataset/test (leak) · sample/per_image/none/fixed (not data-fitted, leakage-free). stage: before_split / after_split.

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".

Phase 4 — Gate the manifest (deterministic)

python3 scripts/check_preprocessing_leakage.py --manifest preprocessing_manifest.json --strict

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?

python3 scripts/check_normalizer_domain.py \
    --profile eda/<cohort>_profile.json \
    --contract work/nnUNet_results/.../plans.json \
    --splits external_mri --out qc/normalizer_domain.json --strict

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-review model_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.