ML / DL method coverage map

Which machine-learning and deep-learning method families the toolkit covers, and how — by selecting, producing, validating, interpreting, and reporting each, integrating the standard frameworks (timm / MONAI / nnU-Net / TorchIO / scikit-learn / xgboost / pyradiomics) rather than reimplementing them. The target user fine-tunes existing models and builds classical-ML on collected clinical data to derive clinical results and write papers — not novel architecture development (that stays out of scope).

Two facts make "all methods" tractable without a skill per algorithm:

1. Produce paths integrate whole libraries. model-scaffold wires timm (hundreds of pretrained backbones) + MONAI / nnU-Net / torchvision; radiomics-ml wires scikit-learn + xgboost / lightgbm / catboost + pyradiomics. Any learner in those libraries is in scope.
2. The rigor gates are learner-agnostic. check_radiomics_ml, check_split_leakage, check_preprocessing_leakage, check_metric_reporting, check_explainability_report, and check_uncertainty_reporting audit the pipeline (leakage, nested CV, calibration, metric choice, saliency rigor, deployment uncertainty) — not the specific algorithm — so they apply to every method in the same family. Deployment-framed models of any family route through uncertainty-imaging (calibrated uncertainty / OOD / abstention) as a cross-cutting safety layer.

Deep learning (imaging)

Family	Examples	Select	Produce / fine-tune	Validate	Interpret	Report / evaluate
Classification CNN / transformer	ResNet, DenseNet, EfficientNet, ViT, Swin (timm)	architecture-zoo	model-scaffold --task classification / --task finetune (transfer learning)	model-validation, preprocess-imaging	explainability	model-evaluation, check-reporting (CLAIM / TRIPOD+AI)
Segmentation	U-Net, 3D U-Net, Attention/Residual U-Net, nnU-Net	architecture-zoo	model-scaffold --task segmentation	model-validation	explainability	model-evaluation
Detection	Faster R-CNN, RetinaNet, YOLO, Mask R-CNN	architecture-zoo	model-scaffold --task detection	model-validation	—	model-evaluation (FROC / mAP)
Promptable / foundation segmentation	SAM, MedSAM, TotalSegmentator	architecture-zoo	model-scaffold --task finetune + MedSAM adapter recipe (finetuning_guide.md)	model-validation	explainability	model-evaluation
Self-supervised pretraining	DINO, MAE, SimCLR	architecture-zoo	model-scaffold --task ssl	model-validation	—	—
Generative / synthesis	GAN, diffusion	architecture-zoo	model-scaffold --task synthesis; train-only diffusion augmentation (finetuning_guide.md)	model-validation	—	check-reporting
Vision-language / multimodal	CLIP, BiomedCLIP	architecture-zoo	model-scaffold --task finetune (transfer learning)	model-validation	—	model-evaluation
Graph neural nets (connectomes)	GCN, GraphSAGE, GAT, GIN, BrainGNN	architecture-zoo (graph.md)	integrate PyTorch Geometric / DGL directly (no model-scaffold graph template)	model-validation (subject-level split); p≫n rigor → radiomics-ml	explainability (attention / salient-ROI sanity)	check-reporting (TRIPOD+AI)

Classical / statistical ML (radiomics & tabular)

All of the below are produced and gated by radiomics-ml (learner-agnostic check_radiomics_ml

• nested-CV recipe), with calibration / clinical-utility from analyze-stats and CLEAR / TRIPOD+AI / PROBAST-AI reporting from check-reporting.

Family	Examples
Penalised regression	LASSO, ridge, elastic-net logistic
Margin / kernel	linear & RBF SVM
Instance-based	k-NN
Probabilistic / discriminant	naive Bayes, LDA, QDA
Single tree	decision tree, CART
Bagging	random forest, extra-trees
Boosting	XGBoost, LightGBM, CatBoost, HistGBM, AdaBoost
Shallow neural	MLP
Meta / ensemble	stacking, voting, blending
Dimensionality reduction	PCA, UMAP, t-SNE, LASSO-selection
Unsupervised / clustering	k-means, hierarchical, GMM
Survival ML	random survival forest, Cox-net, DeepSurv (+ analyze-stats survival)
Probability calibration	Platt, isotonic (+ analyze-stats calibration)

LLM / MLLM

Family	Select / produce	Evaluate	Report
Clinical LLM / multimodal LLM (API or open weights)	prompt-driven; design-ai-benchmarking for reader panels	mllm-eval (faithfulness, hallucination, contamination, clinical-efficacy metrics)	check-reporting (TRIPOD-LLM / MI-CLEAR-LLM / CLAIM)

What is deliberately NOT here

• Novel architecture development / a new training framework. We wire and report MONAI / nnU-Net / timm / scikit-learn; we do not reimplement them or invent architectures.
• Autonomous training / experiment tracking (MLOps). Left to the frameworks + W&B / MLflow; a thin integration reference is roadmap Item 6.
• Anything that runs a model on real patient data or fabricates a metric. Every number comes from the researcher's executed code.

Candidate gaps (open): none outstanding — the six-item model-engineering produce-side depth roadmap is complete, and the architecture-zoo graph-neural-net entry for brain-connectome studies has landed (references/graph.md: GCN / GraphSAGE / GAT / GIN / BrainGNN; integrate PyTorch Geometric / DGL, no model-scaffold graph template). The fine-tuning / SAM-adaptation / diffusion-augmentation produce path landed in model-scaffold --task finetune + references/finetuning_guide.md. See roadmap_model_engineering_depth.md.