通过语言介导的鲁棒嵌入生成减轻组织病理学中的批次效应
Mitigating Batch Effects in Histopathology via Language-Mediated Robust Embedding Generation
AI总结：
提出GLMP框架，利用通用多模态大语言模型将组织学图像转化为文本表示，再生成数值嵌入，有效抑制批次效应，提升跨机构泛化能力。
摘要：
病理基础模型在临床和科学应用中展现出巨大潜力，但其性能常受批次效应影响——即不同组织来源机构间的非生物变异会扭曲学习到的特征表示并损害泛化能力。传统的缓解策略（如染色归一化）在处理这些高维复杂伪影时效果有限。我们提出GLMP（通用大语言模型介导的病理模型），这是一种新颖框架，通过中间文本表示从组织学图像块生成鲁棒的数值嵌入。通过利用预训练的通用多模态大语言模型和文本编码器，GLMP有效优先考虑生物学意义的信号而非机构特异性伪影，从而提升跨机构泛化能力。据我们所知，GLMP是首个使用组织学特征文本描述作为中间表示，从组织学图像生成数值嵌入的病理模型。我们的结果凸显了广域非专用多模态大语言模型在计算病理学中未被开发的潜力，并引入了构建通用、可泛化且鲁棒的病理模型的新范式。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
