用文本描述辅助生成病理图像嵌入，有效减少机构差异干扰。
Mitigating Batch Effects in Histopathology via Language-Mediated Robust Embedding Generation
- 通过中间文本表示生成鲁棒的病理图像嵌入
- 在多个机构数据上提升模型泛化能力，显著降低批次效应
- 首次将通用多模态大模型用于病理嵌入生成，适合跨机构研究
病理基础模型（PFMs）在临床和科研中展现出巨大潜力，但其性能常受批次效应影响——即不同组织来源机构（TSIs）带来的非生物性变异，扭曲了学习到的特征表示并损害泛化能力。传统方法如染色归一化在应对高维复杂伪影时效果有限。我们提出GLMP（通用多模态大语言模型介导的病理模型），利用预训练的通用多模态大语言模型（MLLMs）和文本编码器，将组织学图像块转换为中间文本描述，再生成数值嵌入。该方法有效区分生物学信号与机构特异性伪影，提升跨机构泛化能力。据我们所知，GLMP是首个以组织学特征文本描述作为中间表示，从图像生成数值嵌入的病理模型。结果表明，广泛领域、非专业化的多模态大模型在计算病理学中具有未被发掘的潜力，并为构建通用、可泛化、鲁棒的病理模型开辟新范式。
原文摘要 · Abstract (English)
Pathology foundation models (PFMs) have demonstrated strong potential across clinical and scientific applications, yet their performance is often hindered by batch effects, which are non-biological variations across tissue source institutions (TSIs) that distort learned feature representations and impair generalization. Conventional mitigation strategies, such as stain normalization, offer limited success in addressing these high-dimensional, complex artifacts. We present GLMP (General-purpose LLM-Mediated Pathology model), a novel framework that generates robust numerical embeddings from histology image patches through an intermediate textual representation. By leveraging pretrained general-purpose multimodal large language models (MLLMs) and text encoders, GLMP effectively prioritizes biologically meaningful signals over TSI-specific artifacts, thereby improving cross-institutional generalization. To our knowledge, GLMP is the first pathology model to use text descriptions of histological features as an intermediate representation for generating numerical embeddings from histology images. Our results highlight the untapped potential of broad-domain, non-specialized MLLMs in computational pathology and introduce a new paradigm for building versatile, generalizable, and robust pathology models.
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