SPIDER: A Comprehensive Multi-Organ Supervised Pathology Dataset and
  Baseline Models
Authors: name: Dmitry Nechaev, name: Alexey Pchelnikov, name: Ekaterina Ivanova

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