Deep generative classification of blood cell morphology.

ABSTRACT: Blood cell morphology assessment via light microscopy constitutes a cornerstone of haematological diagnostics, providing crucial insights into diverse pathological conditions. This complex task demands expert interpretation owing to subtle morphological variations, biological heterogeneity and technical imaging factors that obstruct automated approaches. Conventional machine learning methods using discriminative models struggle with domain shifts, intraclass variability and rare morphological variants, constraining their clinical utility. We introduce CytoDiffusion, a diffusion-based generative classifier that faithfully models the distribution of blood cell morphology, combining accurate classification with robust anomaly detection, resistance to distributional shifts, interpretability, d

SUBMITTER: Deltadahl S 

PROVIDER: S-EPMC12629977 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature