Hibou: A Family of Foundational Vision Transformers for Pathology
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
6666b9178a270cedd5c2b8b9
Dmitry Nechaev
extracted_confirmed
2024-06-10T09:49:38.363Z
6308bae5c038bf42d56a98e5
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/yrslTwUe_vy_ZJha1H83m.png
Dmitry Nechaev
user
mgvz
6666b9178a270cedd5c2b8ba
Alexey Pchelnikov
extracted_confirmed
2024-06-10T10:33:58.043Z
6655b0b9d6c043f39719eaaf
/avatars/66138e67ef3be41f29857b285b37adff.svg
Alex Pchelnikov
user
alpchel
6666b9178a270cedd5c2b8bb
Ekaterina Ivanova
admin_assigned
2024-06-13T20:26:47.083Z
6454532bc13cdb83f10e2824
/avatars/98e0d356054c424b075beba039147f24.svg
Ekaterina Ivanova
user
katerinius
Hibou: A Family of Foundational Vision Transformers for Pathology
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
Hibou family of transformers using DINOv2 framework for digital pathology analysis achieves superior performance on a wide range of benchmarks.
foundational transformer pretraining Hibou family vision transformers DINOv2 framework whole slide images WSIs patch-level slide-level benchmarks