# SPIDER: A Comprehensive Multi-Organ Supervised Pathology Dataset and Baseline Models

arXiv: https://arxiv.org/abs/2503.02876 · HTML: https://mlsanity.com/paper/2503.02876/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 4 Mar 2025.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Rotation Equivariant CNNs for Digital Pathology](https://mlsanity.com/paper/1806.03962/) (2018)
- [BACH: Grand Challenge on Breast Cancer Histology Images](https://mlsanity.com/paper/1808.04277/) (2018)
- [Hibou: A Family of Foundational Vision Transformers for Pathology](https://mlsanity.com/paper/2406.05074/) (2024)
- [A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Models](https://mlsanity.com/paper/2408.14496/) (2024)
- [Segmentation by Factorization: Unsupervised Semantic Segmentation for Pathology by Factorizing Foundation Model Features](https://mlsanity.com/paper/2409.05697/) (2024)

## Built on by (most cited first, 13 of 13)

- [PixCell: A generative foundation model for digital histopathology images](https://mlsanity.com/paper/2506.05127/) (2025)
- [TICON: A Slide-Level Tile Contextualizer for Histopathology Representation Learning](https://mlsanity.com/paper/2512.21331/) (2025)
- [Streamline pathology foundation model by cross-magnification distillation](https://mlsanity.com/paper/2509.23097/) (2025)
- [Weakly Supervised Teacher-Student Framework with Progressive Pseudo-mask Refinement for Gland Segmentation](https://mlsanity.com/paper/2603.08605/) (2026)
- [CytoSyn: a Foundation Diffusion Model for Histopathology -- Tech Report](https://mlsanity.com/paper/2603.18089/) (2026)
- [Mitosis Detection in the Wild: Multi-Tumor and Context-Aware Generalization in the MIDOG 2025 Challenge](https://mlsanity.com/paper/2606.07368/) (2026)
- [Do Pathology Foundation Models Encode Disease Progression? A Pseudotime Analysis of Visual Representations](https://mlsanity.com/paper/2601.21334/) (2026)
- [Central-to-Local Adaptive Generative Diffusion Framework for Improving Gene Expression Prediction in Data-Limited Spatial Transcriptomics](https://mlsanity.com/paper/2603.26827/) (2026)
- [STREAM: Stochastic Riemannian Flow Matching with Anisotropic Decoder for Digital Histopathology Image Generation](https://mlsanity.com/paper/2606.07036/) (2026)
- [Role-Guided MOE for Encoder-Level Pathology Representation Learning in WSI Classification](https://mlsanity.com/paper/2609.34897/) (2026)
- [HERO: Histology Encoder for Robust Representation in Oncology](https://mlsanity.com/paper/2609.35943/) (2026)
- [Scaling Multi-Teacher Distillation for Digital Pathology](https://mlsanity.com/paper/2610.12574/) (2026)
- [Beyond Sparse Captions: Aligning Slide-Level Text and Patch-Level Vision in Pathology](https://mlsanity.com/paper/2610.17233/) (2026)

## Abstract (arXiv)

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, Thorax, and Breast 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