S-IGTD: supervised tabular-to-image topology learning via between-group correlation for multiclass classification of biological data (opens in new tab)

Motivation: Tabular-to-image methods allow convolutional neural network (CNN)-based classifiers to analyse high-dimensional biological tables by mapping features onto a two-dimensional grid. Existing layouts are usually driven by unsupervised global correlation, which can place class-discriminative features far apart when nuisance or housekeeping covariation dominates the total covariance structure. Results: We present the Supervised Image Generator for Tabular Data (S-IGTD), a supervised ext...