CytoFormer：用于组织病理学细胞分类的分子监督细胞基础模型
CytoFormer: A Molecularly Supervised Cell Foundation Model for Histopathology Cell Classification
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中文总结 AI 辅助
CytoFormer利用配对H&E染色与空间转录组学数据训练细胞基础模型，在细胞分类、迁移学习及主动学习中表现优异，为常规组织学的细胞分析提供高效可复用表示。
中文摘要 AI 辅助
直接从常规苏木精-伊红（H&E）组织染色中识别细胞类型可实现大规模单细胞分析，但此类模型的训练依赖病理学家的手动标注，对于许多细胞类型而言速度慢、成本高且不可靠。相反，我们用分子对形态进行监督。基于成像的空间转录组学可在切片上原位分析单个细胞，之后该切片可进行H&E染色，从而对同一物理细胞同时观测分子身份和形态。我们组装了81个此类配对的Xenium切片，覆盖16个器官，通过聚类、标记基因标注、器官层面的人工审核和质量控制得到细胞标签，并将其映射到每个器官中常见的细胞类型。这产生了1540万个细胞，每个细胞都有配对的H&E图像块及23种细胞类型之一，我们在此基础上训练了CytoFormer，这是一种带有多任务、器官层面分类头的细胞基础模型。在空间留出的组织上，CytoFormer在全部16个器官中达到0.85的准确率和0.78的宏F1值，其预测结果重现了整个留出切片的组织结构。该表示还可迁移：在编码器冻结的情况下，基于CytoFormer特征的线性头在四个专家标注基准上的表现优于六个病理基础模型，包括在预训练未包含的器官和细胞类型上。最后，在交互式主动学习设置中，CytoFormer的嵌入比现有病理基础模型的标签效率显著更高，仅用少量标注就能从相似肿瘤中检测正常上皮，F1值达0.82，比最强基线的F1值高0.13。CytoFormer将配对的H&E与空间转录组学转化为可复用、标签高效的常规组织学细胞层面分析表示。
英文摘要
Identifying cell types directly from routine haematoxylin and eosin (H&E) histology would enable single-cell analysis at scale, but training such models has relied on manual pathologist annotations, which are slow, expensive and unreliable for many cell types. We instead supervise morphology with molecules. Imaging-based spatial transcriptomics profiles individual cells in situ on a section that can afterwards be stained with H&E, so that molecular identity and morphology are observed for the same physical cell. We assembled 81 such paired Xenium sections spanning 16 organs, derived per-cell labels by clustering, marker-gene annotation, organ-wise human review and quality control, and mapped them onto the cell types commonly reported in each organ. This yielded 15.4 million cells, each with a paired H&E image patch and one of 23 cell types, on which we trained CytoFormer, a cell foundation model with a multi-task, per-organ classification head. On spatially held-out tissue CytoFormer reached an accuracy of 0.85 and a macro-F1 of 0.78 across all 16 organs, and its predictions reproduced the tissue architecture of an entire held-out section. The representation also transfers: with the encoder frozen, a linear head on CytoFormer features performed better than six pathology foundation models on four expert-annotated benchmarks, including on organs and cell types that were not part of pretraining. Finally, in an interactive active-learning setting, CytoFormer's embeddings are markedly more label-efficient than existing pathology foundation models, detecting normal epithelium amid look-alike tumour with an F1 of 0.82 from only a few annotations and leading the strongest baseline by 0.13 in F1. CytoFormer turns paired H&E and spatial transcriptomics into a reusable, label-efficient representation for cell-level analysis of routine histology.
发表机构
- University of Pennsylvania（宾夕法尼亚大学）
- Germantown Friends School（日耳曼敦贵格会学校）
机构由 AI 辅助整理，请以论文原文为准。
