Model-Scaffold Skill

Model-Scaffold Skill

Purpose

This skill stamps out a runnable PyTorch training repo for a medical-imaging task — --task segmentation (U-Net), classification (CNN / timm backbone), detection (torchvision Faster R-CNN / FPN), synthesis (Pix2Pix generator + PatchGAN), ssl (SimCLR encoder), or finetune (transfer-learning a pretrained backbone with a frozen→unfrozen schedule + a provenance record) — with the reproducibility guarantees baked in by construction — so the build is leakage-safe and reproducible before a single epoch runs. It is the imaging analogue of how /analyze-stats generates runnable statistical code: the generator produces the repo, you run the training on your GPU / Colab, and the lane's deterministic gates verify the network-free parts.

It is the missing middle link in the lane: /model-selection (choose) → model-scaffold (build) → /model-assessment (validate the split / design, compute metrics) → /analyze-stats → /write-paper + /check-reporting (publish). It integrates MONAI / nnU-Net / TorchIO (referenced in the generated requirements.txt); it does not reimplement them.

When to use

• You have a data manifest (one row per image, with a patient/subject ID) and want a reproducible, leakage-safe starting repo for a segmentation, classification, detection, synthesis or self-supervised model.
• You want to fine-tune a pretrained backbone (transfer learning — the common clinician workflow: a timm / MONAI / MedSAM checkpoint adapted to your collected clinical data) with the freeze schedule, discriminative learning rates, and pretrained-weight provenance recorded (--task finetune).

When NOT to use

• Auditing an already-trained model's validation design → /model-assessment.
• Held-out metrics / calibration / bootstrap CIs → /model-assessment then /analyze-stats.
• Choosing the architecture for the research question → /model-selection.
• Reimplementing MONAI / nnU-Net → out of scope (the scaffold integrates them).
• LLM / MLLM evaluation → /mllm-eval.

Workflow

Phase 1 — Prepare the manifest

A CSV with one row per image and a patient/subject ID column (patient_id / subject_id / case_id), plus image and label path columns. The ID column is load-bearing: the split is done at the patient level off this column. IDs are compared after stripping surrounding whitespace (P01 and P01 are one patient), in the split and in the generated dataset.py alike.