imaging-data

Imaging-Data Skill

The dataset decides more of a study than the architecture does, and it decides it first. Phases 1–3 establish what the data is and what it will not support, while that is still cheap; Phases 4–7 design and audit the preparation pipeline so it is leakage-safe before /model-scaffold builds the repo. Describe-and-audit only: never modify, resample, reorient, split or write image data, never run preprocessing on real patient data, and wire MONAI / TorchIO transforms by reference rather than writing a new normalisation or resampling implementation.

Elsewhere: tabular/clinical variables → /generate-codebook, /clean-data; auditing the split table, held-out metrics, calibration, subgroup results → /model-assessment; choosing an architecture → /model-selection; building the repo → /model-scaffold.

Workflow

Phase 1 — Profile every case

python3 ${CLAUDE_SKILL_DIR}/scripts/profile_imaging_dataset.py \
    --split train:imagesTr:labelsTr \
    --split test:imagesTs \
    --dataset "MSD Task09 Spleen" \
    --declared-labels 0=background,1=spleen \
    --target-label 1 \
    --plan resample=true,reorient=false,loss=dice_ce,metrics=dice+hd95 \
    --out eda/profile.json

One record per case: grid, spacing, orientation, intensity percentiles, the label values actually present, foreground fraction, and target volume in mL. A --split given no label directory is recorded as unlabelled — itself a finding. Requires nibabel + numpy; the gate does not. Every profile figure comes from opening the files — never from a dataset's README, a similar dataset, or memory. A README can be wrong about its own label indices; the labels cannot.

--target-label on a multi-structure atlas. Foreground defaults to every non-zero index — the whole annotated anatomy. Measured on the AMOS22 CT cases, that pools to 3.2 % instead of the spleen's 0.20 %, so the pooled figure sits above the 1 % imbalance threshold while the target sits far below it and the imbalance verdicts go quiet exactly where the risk is. Naming the target also makes LABEL_EMPTY mean this case has no spleen. Pass --target-label all for a genuinely multi-class study; leave it out on a multi-structure atlas and the gate raises TARGET_LABEL_UNDECLARED.