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

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arxiv 2505.12120 v1 pith:63XPPR2L submitted 2025-05-17 eess.IV cs.CV

Dmitry Nechaev , Alexey Pchelnikov , Ekaterina Ivanova This is my paper
classification eess.IVcs.CV
keywords datasethistaipathologyclinicalcomputationaldatasetsimagelarge-scale
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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.

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Cited by 10 Pith papers

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