Imaging-Data Skill

The dataset decides more of a study than the architecture does, and it decides it first. Phases 1–3 establish what the data is and what it will not support, while that is still cheap; Phases 4–7 design and audit the preparation pipeline so it is leakage-safe before /model-scaffold builds the repo. Describe-and-audit only: never modify, resample, reorient, split or write image data, never run preprocessing on real patient data, and wire MONAI / TorchIO transforms by reference rather than writing a new normalisation or resampling implementation.

Elsewhere: tabular/clinical variables → /generate-codebook, /clean-data; auditing the split table, held-out metrics, calibration, subgroup results → /model-assessment; choosing an architecture → /model-selection; building the repo → /model-scaffold.

Workflow