Hibou：用于病理学的基础视觉Transformer系列
Hibou: A Family of Foundational Vision Transformers for Pathology
AI总结：
本文提出Hibou系列病理基础视觉Transformer，基于DINOv2在超百万张全切片图像上预训练，在补丁级和切片级基准上超越现有方法，并开源模型。
摘要：
病理学，即对病变组织的显微镜检查，对于诊断各种医学状况（尤其是癌症）至关重要。传统方法劳动密集且易受人为错误影响。数字病理学将玻璃切片转化为高分辨率数字图像以供计算机算法分析，通过自动化图像分析和大规模数据处理，提高了诊断的准确性、一致性和效率，从而彻底改变了该领域。基础Transformer预训练对于开发稳健、可泛化的模型至关重要，因为它能够从大量未标注数据中学习。本文介绍了用于病理学的Hibou基础视觉Transformer系列，利用DINOv2框架在包含超过100万张全切片图像（WSI）的专有数据集上预训练了两种模型变体Hibou-B和Hibou-L，这些图像代表了多样的组织类型和染色技术。我们的预训练模型在补丁级和切片级基准测试中均表现出优越性能，超越了现有最先进方法。值得注意的是，Hibou-L在多个基准数据集上取得了最高的平均准确率。为了支持该领域的进一步研究和应用，我们开源了Hibou模型，可通过https://github.com/HistAI/hibou访问。
英文摘要：
Pathology, the microscopic examination of diseased tissue, is critical for diagnosing various medical conditions, particularly cancers. Traditional methods are labor-intensive and prone to human error. Digital pathology, which converts glass slides into high-resolution digital images for analysis by computer algorithms, revolutionizes the field by enhancing diagnostic accuracy, consistency, and efficiency through automated image analysis and large-scale data processing. Foundational transformer pretraining is crucial for developing robust, generalizable models as it enables learning from vast amounts of unannotated data. This paper introduces the Hibou family of foundational vision transformers for pathology, leveraging the DINOv2 framework to pretrain two model variants, Hibou-B and Hibou-L, on a proprietary dataset of over 1 million whole slide images (WSIs) representing diverse tissue types and staining techniques. Our pretrained models demonstrate superior performance on both patch-level and slide-level benchmarks, surpassing existing state-of-the-art methods. Notably, Hibou-L achieves the highest average accuracy across multiple benchmark datasets. To support further research and application in the field, we have open-sourced the Hibou models, which can be accessed at https://github.com/HistAI/hibou.
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。
