architecture-zoo

name	architecture-zoo
description	Choose a model architecture for a medical-imaging research question before scaffolding. Maps the task (classification, segmentation, detection, transfer), modality and dimensionality, labelled-data scale, and class imbalance to a shortlist of architectures, each grounded in its source paper with a when-to-use, a medical-imaging use, a reference implementation, the typical validation setup, and the matching model-scaffold template. Covers the foundational curriculum (ResNet, DenseNet, EfficientNet, ViT, Swin; U-Net, 3-D U-Net, Attention/Residual U-Net, nnU-Net (+ ResEnc/MedNeXt/STU-Net), Mask R-CNN; SAM/MedSAM, nnInteractive/VISTA3D interactive-3D, TotalSegmentator, BiomedCLIP, DINO/MAE/SimCLR; and graph neural nets — GCN/GraphSAGE/GAT/GIN/BrainGNN — for brain connectomes). It teaches archetypes and the task-to-architecture logic (including the "scale the CNN, new≠better" rigour caveat), not a live SOTA leaderboard.
triggers	architecture zoo, which architecture, choose a model, model selection, ResNet vs ViT, U-Net vs nnU-Net, what backbone, foundation model for, transfer learning choice, MedSAM, TotalSegmentator, DINO, MAE, self-supervised, graph neural network, GNN, brain connectome, GCN, GAT, GraphSAGE, BrainGNN, population graph, paper to architecture, reference implementation, when to use ViT, segmentation architecture, classification backbone, nnU-Net ResEnc, MedNeXt, STU-Net, nnInteractive, VISTA3D, SAM-Med3D, Mamba, U-Mamba, interactive segmentation, labelling acceleration, promptable segmentation, nnDetection, lesion detection, ConvNeXt, YOLO, YOLOv8, RT-DETR, DETR, RetinaNet, detection architecture, RETFound, UNI, CONCH, RAD-DINO, Merlin, medical foundation model, pathology foundation model, domain transfer, diffusion model, latent diffusion, ControlNet, MAISI, image synthesis, GAN, CycleGAN, Pix2Pix
tools	Read, Write, Edit, Grep, Glob
model	inherit

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.
• — R-CNN family / Faster R-CNN + FPN / Mask R-CNN / RetinaNet / YOLO / DETR.