model-sourcing
FeaturedVet the concrete third-party model a study will be built on — this repository, this revision, this checkpoint — not the architecture family. Records a model dossier (source and version pin, licence and the file it was read from, intended use, pretrained-weight provenance, model task vs study task, reported validation, what the model was developed on, your evaluation arms) and gates it deterministically. Catches what a licence check and a citation count cannot: an evaluation arm sitting on the benchmark the model was developed or tuned on, so the arm reads like validation while being closer to a training-set score. Also an evaluation set inside a pretraining corpus, an unstated or use-incompatible licence, an unpinned revision, and a hardware claim never executed. It vets an artifact; it never downloads or runs one.
Install
Quality Score: 95/100
Skill Content
Details
- Author
- Aperivue
- Repository
- Aperivue/medsci-skills
- Created
- 3 months ago
- Last Updated
- today
- Language
- Python
- License
- MIT
Integrates with
Bundled in these plugins
Similar Skills
Semantically similar based on skill content — not just same category
architecture-zoo
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.
model-validation
Design or audit the clinical-validation study for an engineer-built medical-imaging model (segmentation, classification, or detection) before the validation report or manuscript is written. Covers patient-level split disjointness and the data-leakage taxonomy, tuning-on-test, internal versus genuine external validation, comparator design, single-run versus multi-seed variance, task-correct metric selection, test-set sizing, and CLAIM 2024 / TRIPOD+AI / STARD-AI reporting fit. Ships a deterministic split-leakage gate that proves patient disjointness by set arithmetic on the emitted split-assignment table. Does not build or train models — it integrates with MONAI / nnU-Net, it does not replace them.
model-scaffold
Generate a reproducible, runnable PyTorch training repo for a medical-imaging task — segmentation, classification, detection, image-to-image synthesis, self-supervised pretraining, or fine-tuning a pretrained backbone (transfer learning) — the missing middle link between choosing an architecture and validating a trained model. Emits a patient-level seed-locked split as an auditable artifact, a task-appropriate model, train and evaluate scripts that seed every RNG and infer under eval mode, a config, requirements, a reproducibility record, and a Methods stub with VERIFY placeholders (no fabricated numbers). Fine-tuning mode adds a frozen-then-unfrozen schedule, discriminative learning rates, and a pretrained-weight provenance record. The reproducibility guarantees hold by construction, so the build is leakage-safe before any training runs. Integrates with MONAI, nnU-Net, TorchIO, timm, and torchvision — it does not reimplement them.