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-B model, which can be accessed at
this https URL

通过数百万激光制片和荧光制片图像进行预训练，本论文介绍了基于 DINOv2 框架的 Hibou 视觉转换器系列，展示了在补丁级别和幻灯片级别基准测试中优于现有方法的卓越性能。