Imaging Data Profiling & Preprocessing
/imaging-dataNEWWhat it does
Profiles a DICOM/NIfTI dataset (spacing, orientation, intensity, labels) against the plan, then designs and audits preprocessing and augmentation for leakage.
Highlights
- ✓Catches a "test set" that carries no ground truth
- ✓Flags label/image grid mismatch and stray label indices
- ✓Data-stage leakage gate (normaliser fit on train only)
- ✓Integrates MONAI / TorchIO transforms
Install this skill
git clone https://github.com/Aperivue/medsci-skills.git
mkdir -p ~/.claude/skills
cp -r medsci-skills/skills/imaging-data ~/.claude/skills/Related skills
Paper-grounded architecture choice for medical imaging, then a check of the actual repo or checkpoint: licence, version pin, weight provenance and benchmark overlap.
Model Scaffold/model-scaffoldGenerate a reproducible, runnable PyTorch training repo for a medical-imaging task — segmentation, classification, detection, synthesis, self-supervised pretraining, or fine-tuning a pretrained backbone — with a patient-level seed-locked split, train/evaluate scripts, and a Methods stub. Integrates MONAI / nnU-Net, never reimplements them.
Model Assessment/model-assessmentValidation design, task-correct held-out metrics (Dice + HD95, AUROC + AUPRC, FROC, calibration), uncertainty/OOD and explainability for trained imaging models, each with a gate.
Model Card & Datasheet/model-cardGenerate the documentation an engineer-built model must carry — a Model Card (Mitchell et al. 2019), a Datasheet for its dataset (Gebru et al. 2021), and a METRIC data-quality pass — filled only from user-supplied facts, then verify every required section is present with a completeness gate.