title: Hibou: A Family of Foundational Vision Transformers for Pathology
summary: 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
  https://github.com/HistAI/hibou
paper_authors._id: 6666b9178a270cedd5c2b8b9
paper_authors.name: Dmitry Nechaev
paper_authors.status: extracted_confirmed
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paper_authors.fullname: Dmitry Nechaev
paper_authors.type: user
paper_authors.user: mgvz
paper_authors._id: 6666b9178a270cedd5c2b8ba
paper_authors.name: Alexey Pchelnikov
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-06-10T10:33:58.043Z
paper_authors._id: 6655b0b9d6c043f39719eaaf
paper_authors.avatarUrl: /avatars/66138e67ef3be41f29857b285b37adff.svg
paper_authors.fullname: Alex Pchelnikov
paper_authors.type: user
paper_authors.user: alpchel
paper_authors._id: 6666b9178a270cedd5c2b8bb
paper_authors.name: Ekaterina Ivanova
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-13T20:26:47.083Z
paper_authors._id: 6454532bc13cdb83f10e2824
paper_authors.avatarUrl: /avatars/98e0d356054c424b075beba039147f24.svg
paper_authors.fullname: Ekaterina Ivanova
paper_authors.type: user
paper_authors.user: katerinius
paper_title: Hibou: A Family of Foundational Vision Transformers for Pathology
paper_summary: 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
  https://github.com/HistAI/hibou
paper_ai_summary: Hibou family of transformers using DINOv2 framework for digital pathology analysis achieves superior performance on a wide range of benchmarks.
paper_ai_keywords: foundational transformer pretraining Hibou family vision transformers DINOv2 framework whole slide images WSIs patch-level slide-level benchmarks