Accurate classification of haematological cells is critical for diagnosing blood disorders, but presents significant challenges for machine automation owing to the complexity of cell morphology, heterogeneities of biological, pathological, and imaging characteristics, and the imbalance of cell type frequencies. We introduce CytoDiffusion, a diffusion-based classifier that effectively models blood cell morphology, combining accurate classification with robust anomaly detection, resistance to distributional shifts, interpretability, data efficiency, and superhuman uncertainty quantification. Our approach outperforms state-of-the-art discriminative models in anomaly detection (AUC 0.976 vs. 0.919), resistance to domain shifts (85.85% vs. 74.38% balanced accuracy), and performance in low-data regimes (95.88% vs. 94.95% balanced accuracy). Notably, our model generates synthetic blood cell images that are nearly indistinguishable from real images, as demonstrated by a Turing test in which expert haematologists achieved only 52.3% accuracy (95% CI: [50.5%, 54.2%]). Furthermore, we enhance model explainability through the generation of directly interpretable counterfactual heatmaps. Our comprehensive evaluation framework, encompassing these multiple performance dimensions, establishes a new benchmark for medical image analysis in haematology, ultimately enabling improved diagnostic accuracy in clinical settings. Our code is available at https://github.com/Deltadahl/CytoDiffusion.

该研究针对血液疾病诊断中血细胞分类的准确性问题，提出了一种名为CytoDiffusion的扩散分类器，能够有效建模血细胞形态。该方法在异常检测、抵抗分布转移和低数据环境下的表现超越了当前最先进的模型，且生成的合成血细胞图像与真实图像几乎无法区分，从而为临床诊断的准确性提供了显著提升。

血细胞形态的深度生成分类