# HISTAI: An Open-Source, Large-Scale Whole Slide Image Dataset for Computational Pathology

arXiv: https://arxiv.org/abs/2505.12120 · HTML: https://mlsanity.com/paper/2505.12120/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 8 references that are in the map.
- First posted on arXiv 17 May 2025.

## Builds on (most cited first, 3 of 3)

- [Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge](https://mlsanity.com/paper/1807.08284/) (2018)
- [Hibou: A Family of Foundational Vision Transformers for Pathology](https://mlsanity.com/paper/2406.05074/) (2024)
- [A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks](https://mlsanity.com/paper/2501.15724/) (2025)

## Built on by (most cited first, 14 of 14)

- [Morphology-Aware Prognostic model for Five-Year Survival Prediction in Colorectal Cancer from H&E Whole Slide Images](https://mlsanity.com/paper/2510.14800/) (2025)
- [A Generative Foundation Model for Multimodal Histopathology](https://mlsanity.com/paper/2604.03635/) (2026)
- [Beyond ViT Tokens: Masked-Diffusion Pretrained Convolutional Pathology Foundation Model for Cell-Level Dense Prediction](https://mlsanity.com/paper/2605.08276/) (2026)
- [StainNet: Scaling Self-Supervised Foundation Models on Immunohistochemistry and Special Stains for Computational Pathology](https://mlsanity.com/paper/2512.10326/) (2025)
- [PathoScribe: Transforming Pathology Data into a Living Library with a Unified LLM-Driven Framework for Semantic Retrieval and Clinical Integration](https://mlsanity.com/paper/2603.08935/) (2026)
- [A Breast Vision Pathology Foundation Model for Real-world Clinical Utility](https://mlsanity.com/paper/2605.08207/) (2026)
- [Simple Token-Efficient Vision-Language Model for Case-level Pathology Synoptic Report Generation](https://mlsanity.com/paper/2605.30716/) (2026)
- [A Pathology Foundation Model for Gastric Cancer with Real-World Validation](https://mlsanity.com/paper/2606.04792/) (2026)
- [DaX: Learning General Pathology Representations Across Scales](https://mlsanity.com/paper/2606.06983/) (2026)
- [ALICE: Learning a General-Purpose Pathology Foundation Model from Vision, Vision-Language, and Slide-Level Experts](https://mlsanity.com/paper/2607.09526/) (2026)
- [InfoDPP-PAC: Principled Patch Selection for Whole Slide Image Analysis](https://mlsanity.com/paper/2608.23574/) (2026)
- [Benchmarking Vision-Language Models for Automated Pathology Diagnosis and Report Generation](https://mlsanity.com/paper/2609.00866/) (2026)
- [Lumen: Parameter-Efficient Alignment of Pretrained Vision and Language Encoders for Zero-Shot Computational Pathology](https://mlsanity.com/paper/2609.17868/) (2026)
- [Beyond Sparse Captions: Aligning Slide-Level Text and Patch-Level Vision in Pathology](https://mlsanity.com/paper/2610.17233/) (2026)

## Abstract (arXiv)

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.