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<p>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, data efficiency and uncertainty quantification that surpasses clinical experts. Our approach outperforms state-of-the-art discriminative models in anomaly detection (area under the curve, 0.990 versus 0.916), resistance to domain shifts (0.854 versus 0.738 accuracy) and performance in low-data regimes (0.962 versus 0.924 balanced accuracy). In particular, CytoDiffusion generates synthetic blood cell images that expert haematologists cannot distinguish from real ones (accuracy, 0.523; 95% confidence interval: [0.505, 0.542]), demonstrating good command of the underlying distribution. Furthermore, we enhance model explainability through directly interpretable counterfactual heat maps. Our comprehensive evaluation framework establishes a multidimensional benchmark for medical image analysis in haematology, ultimately enabling improved diagnostic accuracy in clinical settings.</p>

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Deep generative classification of blood cell morphology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月19日

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  • Simon Deltadahl,
  • Julian Gilbey,
  • Christine Van Laer,
  • Nancy Boeckx,
  • Mathie P. G. Leers,
  • Tanya Freeman,
  • Laura Aiken,
  • Timothy Farren,
  • Matthew Smith,
  • Mohamad Zeina,
  • Stephen MacDonald,
  • Daniel Gleghorn,
  • Martijn Schut,
  • Folkert Asselbergs,
  • Sujoy Kar,
  • Sophie Williams,
  • Mickey Koh,
  • Yvonne Henskens,
  • Norbert de Wit,
  • Umberto D’Alessandro,
  • Bubacarr Bah,
  • Ousman Secka,
  • Rajeev Gupta,
  • Sara Trompeter,
  • Christine van Laer,
  • Gordon A. Awandare,
  • Kwabena Sarpong,
  • Lucas Amenga-Etego,
  • Willem H. Ouwehand,
  • James H. F. Rudd,
  • James HF Rudd,
  • Concetta Piazzese,
  • Joseph Taylor,
  • Nicholas Gleadall,
  • Carola-Bibiane Schönlieb,
  • Suthesh Sivapalaratnam,
  • Michael Roberts,
  • Parashkev Nachev

摘要

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, data efficiency and uncertainty quantification that surpasses clinical experts. Our approach outperforms state-of-the-art discriminative models in anomaly detection (area under the curve, 0.990 versus 0.916), resistance to domain shifts (0.854 versus 0.738 accuracy) and performance in low-data regimes (0.962 versus 0.924 balanced accuracy). In particular, CytoDiffusion generates synthetic blood cell images that expert haematologists cannot distinguish from real ones (accuracy, 0.523; 95% confidence interval: [0.505, 0.542]), demonstrating good command of the underlying distribution. Furthermore, we enhance model explainability through directly interpretable counterfactual heat maps. Our comprehensive evaluation framework establishes a multidimensional benchmark for medical image analysis in haematology, ultimately enabling improved diagnostic accuracy in clinical settings.