title: SPIDER: A Comprehensive Multi-Organ Supervised Pathology Dataset and
    Baseline Models
summary: Advancing AI in computational pathology requires large, high-quality, and
  diverse datasets, yet existing public datasets are often limited in organ
  diversity, class coverage, or annotation quality. To bridge this gap, we
  introduce SPIDER (Supervised Pathology Image-DEscription Repository), the
  largest publicly available patch-level dataset covering multiple organ types,
  including Skin, Colorectal, and Thorax, with comprehensive class coverage for
  each organ. SPIDER provides high-quality annotations verified by expert
  pathologists and includes surrounding context patches, which enhance
  classification performance by providing spatial context.
    Alongside the dataset, we present baseline models trained on SPIDER using the
  Hibou-L foundation model as a feature extractor combined with an
  attention-based classification head. The models achieve state-of-the-art
  performance across multiple tissue categories and serve as strong benchmarks
  for future digital pathology research. Beyond patch classification, the model
  enables rapid identification of significant areas, quantitative tissue metrics,
  and establishes a foundation for multimodal approaches.
    Both the dataset and trained models are publicly available to advance
  research, reproducibility, and AI-driven pathology development. Access them at:
  https://github.com/HistAI/SPIDER
paper_authors._id: 67c7bd7149b52e85403758f8
paper_authors.name: Dmitry Nechaev
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-03-05T03:07:52.944Z
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: 67c7bd7149b52e85403758f9
paper_authors.name: Alexey Pchelnikov
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-03-05T03:07:35.092Z
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: 67c7bd7149b52e85403758fa
paper_authors.name: Ekaterina Ivanova
paper_title: SPIDER: A Comprehensive Multi-Organ Supervised Pathology Dataset and
    Baseline Models
paper_summary: Advancing AI in computational pathology requires large, high-quality, and
  diverse datasets, yet existing public datasets are often limited in organ
  diversity, class coverage, or annotation quality. To bridge this gap, we
  introduce SPIDER (Supervised Pathology Image-DEscription Repository), the
  largest publicly available patch-level dataset covering multiple organ types,
  including Skin, Colorectal, and Thorax, with comprehensive class coverage for
  each organ. SPIDER provides high-quality annotations verified by expert
  pathologists and includes surrounding context patches, which enhance
  classification performance by providing spatial context.
    Alongside the dataset, we present baseline models trained on SPIDER using the
  Hibou-L foundation model as a feature extractor combined with an
  attention-based classification head. The models achieve state-of-the-art
  performance across multiple tissue categories and serve as strong benchmarks
  for future digital pathology research. Beyond patch classification, the model
  enables rapid identification of significant areas, quantitative tissue metrics,
  and establishes a foundation for multimodal approaches.
    Both the dataset and trained models are publicly available to advance
  research, reproducibility, and AI-driven pathology development. Access them at:
  https://github.com/HistAI/SPIDER
paper_ai_summary: SPIDER, a large patch-level pathology dataset with diverse organ coverage and high-quality annotations, enhances classification performance using Hibou-L with an attention-based classification head.
paper_ai_keywords: Hibou-L attention-based classification head patch-level dataset tissue categories surrounding context patches digital pathology