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<p>Bone marrow cytomorphology underpins diagnosis of myelodysplastic syndromes and other myeloid neoplasms, but automated classification is limited by strong inter-class similarity, long-tailed class distributions, and annotation ambiguity near developmental boundaries. We propose a biologically aware deep learning framework that encodes hematopoietic structure into both training and inference through: (i) a hierarchical coarse-to-fine classifier, (ii) a neighbor-structured soft auxiliary loss that relaxes penalties for morphologically adjacent classes, (iii) confidence-adaptive soft relabeling that preserves ambiguous samples with blended targets, and (iv) embedding-space k-nearest-neighbor retrieval-augmented inference with validation-only strategy selection. We evaluate under five-fold cross-validation on MK-11 (7,204 megakaryocyte images; 70 patients; official patient-/WSI-wise folds) and a curated 23-class subset of BM Cell MDS (24,811 images; stratified image-level folds). On MK-11, the framework achieves 77.36 ± 6.00% accuracy, 0.7311 ± 0.0355 macro-F1, and 91.65 ± 4.85% top-3 accuracy. On BM Cell MDS, it achieves 81.65 ± 0.27% accuracy, 0.7159 ± 0.0151 macro-F1, and 95.15 ± 1.87% top-3 accuracy. These results suggest that modeling biological locality and ambiguity can yield consistent and clinically interpretable gains without increasing backbone scale and with only a small retrieval-memory and lookup-latency overhead.</p>

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A unified biologically informed framework for bone marrow cell classification under ambiguity and imbalance

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月19日

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  • Zaied Alhaj,
  • Gökçe Koç,
  • Mahmut Ozturk,
  • Noman Q. AL Naggar

摘要

Bone marrow cytomorphology underpins diagnosis of myelodysplastic syndromes and other myeloid neoplasms, but automated classification is limited by strong inter-class similarity, long-tailed class distributions, and annotation ambiguity near developmental boundaries. We propose a biologically aware deep learning framework that encodes hematopoietic structure into both training and inference through: (i) a hierarchical coarse-to-fine classifier, (ii) a neighbor-structured soft auxiliary loss that relaxes penalties for morphologically adjacent classes, (iii) confidence-adaptive soft relabeling that preserves ambiguous samples with blended targets, and (iv) embedding-space k-nearest-neighbor retrieval-augmented inference with validation-only strategy selection. We evaluate under five-fold cross-validation on MK-11 (7,204 megakaryocyte images; 70 patients; official patient-/WSI-wise folds) and a curated 23-class subset of BM Cell MDS (24,811 images; stratified image-level folds). On MK-11, the framework achieves 77.36 ± 6.00% accuracy, 0.7311 ± 0.0355 macro-F1, and 91.65 ± 4.85% top-3 accuracy. On BM Cell MDS, it achieves 81.65 ± 0.27% accuracy, 0.7159 ± 0.0151 macro-F1, and 95.15 ± 1.87% top-3 accuracy. These results suggest that modeling biological locality and ambiguity can yield consistent and clinically interpretable gains without increasing backbone scale and with only a small retrieval-memory and lookup-latency overhead.