HemaHier：链条件有序层级用于谱系感知的骨髓细胞学
HemaHier: Chain-Conditioned Ordinal Hierarchies for Lineage-Aware Bone-Marrow Cytology
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中文总结 AI 辅助
针对骨髓细胞学中谱系和成熟结构被扁平分类器忽略的问题，提出HemaHier序数层级预测头，通过链条件成熟度评分和共享后验实现层级一致预测，在三个数据集上减少严重错误并提供成熟度排序。
中文摘要 AI 辅助
骨髓细胞学本质上具有结构性：每个细胞属于一个造血谱系，且许多细胞类型位于有序的成熟轨迹上。标准的扁平分类器忽略这一结构，将轻微的同谱系混淆与严重的跨谱系错误同等对待，且仅预测离散标签。我们提出HemaHier，一种用于冻结或轻度适应的细胞学基础模型的序数层级预测头。其核心组件是一个链条件成熟度评分，在每条链的查询下读取单一成熟度值，仅对生物学上有效的健康链进行监督，而发育不良和链外细胞类型仍作为类别，但被排除在成熟度监督之外。精细和谱系预测通过共享后验耦合，确保层级一致性，且分阶段目标先稳定识别，再添加谱系和成熟度监督。在共享本体下的三个骨髓数据集上，HemaHier在实现具有竞争力的识别的同时，减少了生物学严重错误，并添加了扁平分类器所缺乏的谱系内成熟度排序。代码可在以下https URL获取。
英文摘要
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.
发表机构
- University of Regensburg（雷根斯堡大学）
- Technical University of Munich（慕尼黑工业大学）
- Munich Center for Machine Learning（慕尼黑机器学习中心）
- Munich Data Science Institute（慕尼黑数据科学研究所）
- RWTH Aachen University（亚琛工业大学）
- Center for Integrated Oncology Aachen Bonn Cologne Düsseldorf（亚琛-波恩-科隆-杜塞尔多夫综合肿瘤中心）
- Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所）
机构由 AI 辅助整理，请以论文原文为准。
