Article

S-IGTD: supervised tabular-to-image topology learning via between-group correlation for multiclass classification of biological data

2026-05-21

Abstract excerpt

<h4>Motivation</h4> 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. <h4>R...

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Literature Corpus work: 74a09762-d626-5fcf-a3c1-f3b00d52645c
DOI: 10.64898/2026.05.19.726105

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