SPIDER: A Comprehensive Multi-Organ Supervised Pathology Dataset and
  Baseline Models
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
67c7bd7149b52e85403758f8
Dmitry Nechaev
extracted_confirmed
2025-03-05T03:07:52.944Z
6308bae5c038bf42d56a98e5
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/yrslTwUe_vy_ZJha1H83m.png
Dmitry Nechaev
user
mgvz
67c7bd7149b52e85403758f9
Alexey Pchelnikov
extracted_confirmed
2025-03-05T03:07:35.092Z
6655b0b9d6c043f39719eaaf
/avatars/66138e67ef3be41f29857b285b37adff.svg
Alex Pchelnikov
user
alpchel
67c7bd7149b52e85403758fa
Ekaterina Ivanova
SPIDER: A Comprehensive Multi-Organ Supervised Pathology Dataset and
  Baseline Models
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
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.
Hibou-L attention-based classification head patch-level dataset tissue categories surrounding context patches digital pathology