HISTAI: An Open-Source, Large-Scale Whole Slide Image Dataset for
  Computational Pathology
Recent advancements in Digital Pathology (DP), particularly through
artificial intelligence and Foundation Models, have underscored the importance
of large-scale, diverse, and richly annotated datasets. Despite their critical
role, publicly available Whole Slide Image (WSI) datasets often lack sufficient
scale, tissue diversity, and comprehensive clinical metadata, limiting the
robustness and generalizability of AI models. In response, we introduce the
HISTAI dataset, a large, multimodal, open-access WSI collection comprising over
60,000 slides from various tissue types. Each case in the HISTAI dataset is
accompanied by extensive clinical metadata, including diagnosis, demographic
information, detailed pathological annotations, and standardized diagnostic
coding. The dataset aims to fill gaps identified in existing resources,
promoting innovation, reproducibility, and the development of clinically
relevant computational pathology solutions. The dataset can be accessed at
https://github.com/HistAI/HISTAI.
682c546e3ef0eea99ddfdd40
Dmitry Nechaev
extracted_confirmed
2025-05-20T11:29:21.418Z
6308bae5c038bf42d56a98e5
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/yrslTwUe_vy_ZJha1H83m.png
Dmitry Nechaev
user
mgvz
682c546e3ef0eea99ddfdd41
Alexey Pchelnikov
extracted_confirmed
2025-05-20T11:06:05.572Z
6655b0b9d6c043f39719eaaf
/avatars/66138e67ef3be41f29857b285b37adff.svg
Alex Pchelnikov
user
alpchel
682c546e3ef0eea99ddfdd42
Ekaterina Ivanova
HISTAI: An Open-Source, Large-Scale Whole Slide Image Dataset for
  Computational Pathology
Recent advancements in Digital Pathology (DP), particularly through
artificial intelligence and Foundation Models, have underscored the importance
of large-scale, diverse, and richly annotated datasets. Despite their critical
role, publicly available Whole Slide Image (WSI) datasets often lack sufficient
scale, tissue diversity, and comprehensive clinical metadata, limiting the
robustness and generalizability of AI models. In response, we introduce the
HISTAI dataset, a large, multimodal, open-access WSI collection comprising over
60,000 slides from various tissue types. Each case in the HISTAI dataset is
accompanied by extensive clinical metadata, including diagnosis, demographic
information, detailed pathological annotations, and standardized diagnostic
coding. The dataset aims to fill gaps identified in existing resources,
promoting innovation, reproducibility, and the development of clinically
relevant computational pathology solutions. The dataset can be accessed at
https://github.com/HistAI/HISTAI.