Bacterial ID stops chasing exact species: pooling 13-species single-cell images into AST-routing groups lifts accuracy from 65.4% to 77.1%
Single-cell image classification struggles to name species on unseen strains. SC-MAPS aligns phase-contrast, bright-field and three fluorescence channels on 916,435 cells from 13 species and fuses them with deep learning; on held-out strains single-cell species accuracy is 65.4%, pooling species probabilities into eight AST-routing groups raises it to 77.1%, and combining five cells from one sample reaches 91.1%.