RudolfV：由病理学家打造、服务于病理学家的基础模型
RudolfV: A Foundation Model by Pathologists for Pathologists
AI总结：
该研究提出融合病理学家知识、半自动数据整理与多实验室多染色数据的病理基础模型 RudolfV，并在多项基准上超越现有最先进模型。
摘要：
人工智能已经开始改变组织病理学，对临床诊断和生物医学研究产生影响。然而，尽管已有许多计算病理学方法被提出，但当前大多数 AI 模型在泛化能力、应用多样性以及罕见疾病处理方面仍存在局限。近期的研究引入了自监督基础模型以应对这些挑战，但现有方法在设计上并未利用病理学家的知识。在本研究中，我们提出了一种设计计算病理学基础模型的新方法，融合病理学家的专业知识、半自动数据整理，以及来自 15 个以上实验室的多样化数据集；该数据集涵盖 58 种组织类型，并包含 129 种不同的组织化学和免疫组织化学染色方式。我们证明，我们的模型“RudolfV”在聚焦肿瘤微环境分析、生物标志物评估和参考病例检索的不同基准上，均超越现有最先进的基础模型，同时表现出良好的鲁棒性。本研究表明，特定领域知识如何提升病理学基础模型的效率和性能，并开辟新的应用领域。
英文摘要：
Artificial intelligence has started to transform histopathology impacting clinical diagnostics and biomedical research. However, while many computational pathology approaches have been proposed, most current AI models are limited with respect to generalization, application variety, and handling rare diseases. Recent efforts introduced self-supervised foundation models to address these challenges, yet existing approaches do not leverage pathologist knowledge by design. In this study, we present a novel approach to designing foundation models for computational pathology, incorporating pathologist expertise, semi-automated data curation, and a diverse dataset from over 15 laboratories, including 58 tissue types, and encompassing 129 different histochemical and immunohistochemical staining modalities. We demonstrate that our model "RudolfV" surpasses existing state-of-the-art foundation models across different benchmarks focused on tumor microenvironment profiling, biomarker evaluation, and reference case search while exhibiting favorable robustness properties. Our study shows how domain-specific knowledge can increase the efficiency and performance of pathology foundation models and enable novel application areas.
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。
