Radiomics ML
/radiomics-mlNEWWhat it does
Produce or audit a radiomics / tabular clinical-ML study — features to a classical learner to a clinical outcome — with a nested-CV, feature-stability, and calibration gate. No GPU.
Highlights
- ✓Nested cross-validation (tuning never on reported folds)
- ✓Feature-stability (ICC) filtering + calibration
- ✓LASSO / SVM / random forest / XGBoost, learner-agnostic
Install this skill
git clone https://github.com/Aperivue/medsci-skills.git
mkdir -p ~/.claude/skills
cp -r medsci-skills/skills/radiomics-ml ~/.claude/skills/Related skills
Paper-grounded architecture choice for medical imaging, then a check of the actual repo or checkpoint: licence, version pin, weight provenance and benchmark overlap.
Model Scaffold/model-scaffoldGenerate a reproducible, runnable PyTorch training repo for a medical-imaging task — segmentation, classification, detection, synthesis, self-supervised pretraining, or fine-tuning a pretrained backbone — with a patient-level seed-locked split, train/evaluate scripts, and a Methods stub. Integrates MONAI / nnU-Net, never reimplements them.
Model Assessment/model-assessmentValidation design, task-correct held-out metrics (Dice + HD95, AUROC + AUPRC, FROC, calibration), uncertainty/OOD and explainability for trained imaging models, each with a gate.
Model Card & Datasheet/model-cardGenerate the documentation an engineer-built model must carry — a Model Card (Mitchell et al. 2019), a Datasheet for its dataset (Gebru et al. 2021), and a METRIC data-quality pass — filled only from user-supplied facts, then verify every required section is present with a completeness gate.