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.