MIPHEI-ViT:使用ViT基础模型从H&E图像中预测多重体免疫光
Guillaume Balezo1, Roger Trullo2, Albert Pla Planas3
1Digital R&D, Sanofi, Paris, 75008, France; Center for Statistics and Images (STIM), Mines Paris - PSL, Fontainebleau, 77300, France; Center for Computational Biology (CBIO), Mines Paris - PSL, Paris, 75006, France.
Computers in biology and medicine
|March 9, 2026
概括
这项研究介绍了MIPHEI,这是一种AI模型,可以从标准的血素和 (H&E) 图像中预测多重免疫光 (mIF) 细胞标记物,从而从常规病理幻灯片中实现先进的癌症分析.
科学领域:
- 计算病理学计算病理学
- 人工智能在瘤学中的应用
- 生物医学图像分析
背景情况:
- 血素和欧 (H&E) 染色是癌症诊断的标准,可视化形态和结构.
- 多重复合免疫光学 (mIF) 提供了精确的细胞识别,但面临临临床采用障碍 (成本,物流).
- 在利用常规的H&E进行详细的细胞类型分析方面存在差距.
研究的目的:
- 开发一个AI模型 (MIPHEI),从H&E图像中预测mIF信号.
- 为了使细胞类型识别和空间分析仅使用H&E数据.
- 在临床环境中弥合常规H&E和先进的mIF分析之间的差距.
主要方法:
- 开发了MIPHEI,这是一种以U-Net为灵感的架构,具有视觉变压器 (ViT) 病理基础模型编码器.
- 在OrionCRC数据集 (结肠直肠癌H&E和mIF图像) 上培训了MIPHEI.
- 在五个独立数据集 (HEMIT,PathoCell,IMMUcan,Lizard,PanNuke) 上验证了MIPHEI.
主要成果:
- MIPHEI从H&E图像中准确地分类了细胞类型,在Pan-CK (0.93) 和α-SMA (0.83) 中获得了高F1分.
- 该模型在CD3e (0.68) 中表现强,并且在大多数测试标记物中表现优于基线.
- 结果表明,MIPHEI捕捉了核形态和细胞类型定义分子标记物之间的复杂关系.
结论:
- MIPHEI成功地预测了来自H&E图像的多重免疫光信号.
- 该模型有助于对大规模的H&E数据集进行细胞类型意识分析.
- 这种方法为将先进的空间生物学见解纳入常规癌症诊断提供了一个有希望的途径.
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