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 a task, and need to know what the data will and will not support before committing to a plan.
• You inherited a dataset and need its integrity established (labels intact, splits labelled, label values as declared) before anyone trains on it.
• You are about to write a Methods section that describes the cohort and its acquisition.

When NOT to use

• Tabular / clinical variables → /generate-codebook (data dictionary) and /clean-data.
• Designing the preprocessing pipeline and auditing it for data-stage leakage → /preprocess-imaging (it consumes what this skill describes).
• Auditing the train/val/test split table → /model-validation.
• Choosing an architecture → /architecture-zoo. Building the repo → /model-scaffold.
• Held-out metrics, calibration, subgroup results → /model-evaluation then /analyze-stats.