title: HISTAI: An Open-Source, Large-Scale Whole Slide Image Dataset for
    Computational Pathology
summary: 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.
paper_authors._id: 682c546e3ef0eea99ddfdd40
paper_authors.name: Dmitry Nechaev
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-20T11:29:21.418Z
paper_authors._id: 6308bae5c038bf42d56a98e5
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/yrslTwUe_vy_ZJha1H83m.png
paper_authors.fullname: Dmitry Nechaev
paper_authors.type: user
paper_authors.user: mgvz
paper_authors._id: 682c546e3ef0eea99ddfdd41
paper_authors.name: Alexey Pchelnikov
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-20T11:06:05.572Z
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: 682c546e3ef0eea99ddfdd42
paper_authors.name: Ekaterina Ivanova
paper_title: HISTAI: An Open-Source, Large-Scale Whole Slide Image Dataset for
    Computational Pathology
paper_summary: 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.