profile-imaging

# Profile-Imaging Skill ## Purpose A dataset decides more of a study than the architecture does, and it decides it **first**. Before anything is preprocessed, split, or trained, a handful of facts are already true about the data, and each one closes off or opens up a research plan: - If the target occupies 0.4 % of the volume, accuracy is not a metric — predicting background everywhere scores 99.6 %. - If through-plane spacing runs 1.5–8 mm inside a single institution, resampling is not a default to accept quietly; it is the most consequential preprocessing choice in the study, and it is also the axis along which an external dataset will differ. - If the directory named `imagesTs` has no labels, it is not a test set, and the held-out set has to come from somewhere else — better known before training than after. - If the organ volume spans 56–502 mL when normal is roughly 100–250, the cohort contains disease that a subgroup analysis should be **pre-specified** for, rather than discovered post hoc. None of that requires a model, a GPU, or an engineer. It requires reading every file once and writing down what is there. This skill does that, and then audits the plan against it. It is the **front door** of the model-engineering lane: `profile-imaging (describe)` → `/design-study` + `/architecture-zoo` (decide) → `/preprocess-imaging` (plan the pipeline) → `/model-scaffold` (build) → `/model-validation` → `/model-evaluation`. ## When to use - You have a dataset and ...