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ConvBayes_new + blitz BayesianLSTM). Given a CT cuboid and a bixel energy, it predicts a local dose cuboid together with a Monte-Carlo predictive variance, estimated from an ensemble of seeded forward passes (the BayesianLSTM samples fresh weights from its posterior on every call).pyRadPlan.ml.load_model and usable directly as the AIBeamletEngine dose-calculation engine in pyRadPlan:pln.prop_dose_calc = {"engine": "AIBeamlet", "model": "pyRadPlan-dosecalc-Bayes-proton-lung"}pyRadPlan.ml):| File | Purpose |
|---|---|
model.py | ConvBayes_new network definition |
preprocessor.py | ConvBayesEnsemblePreprocessor — input assembly + Monte-Carlo ensemble inference + output scaling |
weights.safetensors | Trained weights |
model_config.json | Declarative model/preprocessing/dose-calc configuration |
physical_dose — Monte-Carlo mean dosevariance — predictive variance (Gy²) across the ensemblemodel_preprocessing.ensemble_size in model_config.json), and can be overridden per plan via pln.prop_dose_calc["ensemble_size"].AIBeamletEngine warns when the plan falls outside the declared range.Security note: loading this model executesmodel.py/preprocessor.pyshipped in this repository (gated behindtrust_remote_code, defaultTrueinpyRadPlan.ml).