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<p>Accurate white blood cell (WBC) classification is important for hematological screening and computer-aided blood smear image analysis. Manual microscopic examination is time-consuming and observer-dependent, particularly for subtypes with similar nuclear morphology, cytoplasmic texture, and staining appearance. Deep learning methods have improved automated WBC recognition, but fine-grained subtype classification still requires effective coordination between local morphological details and broader cellular context. We present an edge-prior and reliability-guided collaborative framework for WBC classification. A ConvNeXt branch extracts local morphology, and a Mamba-based branch models long-range context. The Structure-Aided Attention Fusion module uses multi-scale edge priors to align features around nuclear contours and cytoplasmic boundaries. Reliability-guided bilateral fusion adjusts branch contributions using predictive entropy, maximum class probability, and classification margin. Evaluations across three open-access WBC datasets (PBC, LDWBC, Raabin-WBC) return respective image-level classification accuracies at 99.32%, 98.18% and 99.03%. Further tests spanning ablation, transfer learning, robustness and interpretability dissect core performance contributors alongside model stability. The results support the feasibility of the framework for image-level WBC classification on public benchmarks. Full coding resources of our classification framework appear at <a href="https://github.com/si-yuan20/White-blood-cell-classification">https://github.com/si-yuan20/White-blood-cell-classification</a>.</p>

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Edge-prior and reliability-guided collaborative learning for white blood cell classification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月1日

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  • Rong Gao,
  • Qi Ke,
  • Aiquan Li,
  • Xinning Qin,
  • Sichao Zhao

摘要

Accurate white blood cell (WBC) classification is important for hematological screening and computer-aided blood smear image analysis. Manual microscopic examination is time-consuming and observer-dependent, particularly for subtypes with similar nuclear morphology, cytoplasmic texture, and staining appearance. Deep learning methods have improved automated WBC recognition, but fine-grained subtype classification still requires effective coordination between local morphological details and broader cellular context. We present an edge-prior and reliability-guided collaborative framework for WBC classification. A ConvNeXt branch extracts local morphology, and a Mamba-based branch models long-range context. The Structure-Aided Attention Fusion module uses multi-scale edge priors to align features around nuclear contours and cytoplasmic boundaries. Reliability-guided bilateral fusion adjusts branch contributions using predictive entropy, maximum class probability, and classification margin. Evaluations across three open-access WBC datasets (PBC, LDWBC, Raabin-WBC) return respective image-level classification accuracies at 99.32%, 98.18% and 99.03%. Further tests spanning ablation, transfer learning, robustness and interpretability dissect core performance contributors alongside model stability. The results support the feasibility of the framework for image-level WBC classification on public benchmarks. Full coding resources of our classification framework appear at https://github.com/si-yuan20/White-blood-cell-classification.