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<p>Manual differential leukocyte counting is a critical yet time-consuming and observer-dependent process in clinical hematology. This study presents a comparative analysis of You Only Look Once v11 (YOLOv11) and Vision Transformer (ViT) architectures for the classification of 14 leukocyte types and artifacts using a private clinical dataset. We further investigated the impact of HistAuGAN, a domain-specific data augmentation strategy designed to simulate real-world staining variability. Across experimental settings, ViT models achieved higher overall performance than YOLOv11 variants, and the application of HistAuGAN led to systematic improvements in both architectural families. The best-performing configuration, <Emphasis FontCategory="NonProportional">ViT-Base</Emphasis> trained on the HistAuGAN-augmented dataset, achieved a macro F1-score of 98.36% and an overall accuracy of 99.75% on the validation set. To assess generalization capacity, this configuration was additionally evaluated on the public PBC and LISC datasets, demonstrating meaningful cross-dataset performance without architectural modification. Model interpretability was examined through attention- and activation-based saliency analyses, indicating that predictions were primarily driven by morphologically relevant leukocyte regions rather than background structures. These findings suggest that combining global-context modeling with domain-informed augmentation provides a robust and clinically coherent framework for fine-grained leukocyte classification.</p>

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Comparative analysis of convolutional and vision transformer models for automated leukocyte classification enhanced by generative color augmentation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月24日

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  • João Kasprowicz,
  • Alexandre Gonçalves Silva

摘要

Manual differential leukocyte counting is a critical yet time-consuming and observer-dependent process in clinical hematology. This study presents a comparative analysis of You Only Look Once v11 (YOLOv11) and Vision Transformer (ViT) architectures for the classification of 14 leukocyte types and artifacts using a private clinical dataset. We further investigated the impact of HistAuGAN, a domain-specific data augmentation strategy designed to simulate real-world staining variability. Across experimental settings, ViT models achieved higher overall performance than YOLOv11 variants, and the application of HistAuGAN led to systematic improvements in both architectural families. The best-performing configuration, ViT-Base trained on the HistAuGAN-augmented dataset, achieved a macro F1-score of 98.36% and an overall accuracy of 99.75% on the validation set. To assess generalization capacity, this configuration was additionally evaluated on the public PBC and LISC datasets, demonstrating meaningful cross-dataset performance without architectural modification. Model interpretability was examined through attention- and activation-based saliency analyses, indicating that predictions were primarily driven by morphologically relevant leukocyte regions rather than background structures. These findings suggest that combining global-context modeling with domain-informed augmentation provides a robust and clinically coherent framework for fine-grained leukocyte classification.