HemaHier: Chain-Conditioned Ordinal Hierarchies for Lineage-Aware Bone-Marrow Cytology

2026-09-11

AI Summary

为解决骨髓细胞学中细胞类型识别的结构化问题，提出HemaHier方法，通过链条件成熟度评分与层次预测头，提高识别准确性并减少生物学上的严重错误。

Abstract

Bone-marrow cytology is inherently structured: each cell belongs to a hematopoietic lineage, and many cell types lie on ordered maturation trajectories. Standard flat classifiers ignore this structure, treating a mild same-lineage confusion the same as a severe cross-lineage mistake and predicting only discrete labels. We propose HemaHier, an ordinal-hierarchical prediction head for a frozen or lightly adapted cytology foundation model. Its central component is a chain-conditioned maturity score that reads a single maturity value under a per-chain query, supervised only on biologically valid healthy chains, while dysplastic and off-chain cell types remain classes but are excluded from maturity supervision. Fine and lineage predictions are coupled through a shared posterior that guarantees hierarchical consistency, and a staged objective first stabilizes recognition, then adds lineage and maturity supervision. On three bone-marrow datasets under a shared ontology, HemaHier achieves competitive recognition while reducing biologically severe errors and adding a within-lineage maturity ordering that flat classifiers lack. Code is available at https://github.com/xmindflow/HemaHier.

Problem

• Bone-marrow cytology
• Hematopoietic lineage
• Ordered maturation trajectories
• Flat classifiers
• Biological structure

Innovation

• Chain-Conditioned Maturity Score
• Ordinal-Hierarchical Prediction
• Lineage-Aware Cytology
• Biological Validity
• Staged Objective