architecture-zoo

Architecture-Zoo Skill

Purpose

This skill turns a medical-imaging research question into a paper-grounded architecture choice — so the build starts from the right archetype (and a known validation setup) rather than from whatever is fashionable, and the choice carries its source citation into the Methods. It is the front end of the model-engineering lane: architecture-zoo (choose) → /model-scaffold (build) → /model-validation (validate).

It is advisory (Layer D): it writes a short decision note, never code or weights. The actual repo is /model-scaffold. It describes archetypes and the task → family → constraint logic, not a live SOTA leaderboard (SOTA churns; the logic does not).

When to use

• You need to pick an architecture/backbone for a classification, segmentation, detection, or transfer-learning question and want it grounded in the literature with a sensible default.

When NOT to use

• Generating the runnable repo → /model-scaffold.
• Auditing a trained model's validation design → /model-validation.
• Metrics / calibration → /model-evaluation + /analyze-stats.
• General study/validity design → /design-study; AI-vs-expert benchmark → /design-ai-benchmarking.
• LLM / MLLM → /mllm-eval.

Workflow

Phase 1 — Frame the question

State the task (classification / segmentation / detection / transfer), the modality + dimensionality (2-D vs 3-D volume), the labelled-data scale (events / structures, not just images), label availability (lots / few / unlabelled pool), and constraints (class imbalance, small structures, interpretability, deployment compute).

Phase 2 — Walk the decision tree

Open ${CLAUDE_SKILL_DIR}/references/index.md and follow task → constraints → default pick. It routes to a family card.

Phase 3 — Read the family card

• ${CLAUDE_SKILL_DIR}/references/classification.md — ResNet / DenseNet / EfficientNet / Inception / ViT / Swin / DeiT.
• ${CLAUDE_SKILL_DIR}/references/segmentation.md — U-Net / 3-D U-Net / V-Net / Attention & Residual U-Net / nnU-Net / SegResNet / Swin-UNETR / Mask R-CNN.
• ${CLAUDE_SKILL_DIR}/references/detection.md — R-CNN family / Faster R-CNN + FPN / Mask R-CNN / RetinaNet / YOLO / DETR.
• ${CLAUDE_SKILL_DIR}/references/synthesis.md — Pix2Pix / CycleGAN / SPADE / diffusion (DDPM, latent) / VAE / fastMRI reconstruction.
• ${CLAUDE_SKILL_DIR}/references/foundation_models.md — SAM / MedSAM / MedSAM2 / TotalSegmentator / SegVol / BiomedCLIP / DINO / MAE / SimCLR / MoCo.
• ${CLAUDE_SKILL_DIR}/references/graph.md — GCN / GraphSAGE / GAT / GIN / BrainGNN for brain connectomes & population graphs (integrate PyTorch Geometric / DGL; not scaffolded by model-scaffold). Each card gives the paper, core idea, when-to-use, medical-imaging use, reference implementation, and the typical validation/experiment setup for that architecture class.