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ConvDual architecture, single forward pass). Given a CT cuboid and a bixel energy, predicts a local physical dose cuboid and a dose-weighted LET cuboid.pyRadPlan.ml.load_model and usable directly as the AIBeamletEngine dose-calculation engine in pyRadPlan:pln.prop_dose_calc = {"engine": "AIBeamlet", "model": "pyRadPlan-dosecalc-Det-DoseLET-proton"}pyRadPlan.ml):| File | Purpose |
|---|---|
model.py | ConvDual network definition |
preprocessor.py | ConvDoseDualPreprocessor — input assembly + forward pass + channel-split output scaling |
weights.safetensors | Trained weights |
model_config.json | Declarative model/preprocessing/dose-calc configuration |
physical_dose — predicted physical doselet_dose — predicted dose-weighted LETAIBeamletEngine 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).