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2/24/2026

HistAI Expands CellDX DataHub with 50,000+ New Whole Slide Images

HistAI today announced a major expansion of its self-service CellDX DataHub, adding more than 50,000 new whole slide images (WSIs) to the platform.

With this latest update, CellDX DataHub now provides access to over 212,000 digitized slides, including 48,333 IHC slides, further strengthening its position as a scalable infrastructure layer for digital pathology and AI development.

Immediate, Self-Service Access to Structured Pathology Data

  • CellDX DataHub is designed to remove friction from pathology data access. Users can immediately explore:

    1. High-resolution whole slide images (WSIs)

    2. Structured, detailed pathology reports

    3. Searchable and AI-ready datasets

    4. IHC and multi-modal pathology data

The platform enables researchers, AI engineers, and clinical innovators to rapidly access production-scale data without lengthy onboarding or manual data processing.

Access the platform here:
👉 https://celldx.hist.ai

Built for AI-Native Workflows

Beyond direct platform access, CellDX DataHub integrates seamlessly with AI-assisted development environments. Users can query and retrieve data programmatically via AI agents such as Claude Code, Gemini, and Codex using HistAI’s dedicated DataHub skill.

Explore the developer access here:
👉 https://github.com/histai/datahub

This AI-native approach enables:

  • Automated dataset exploration

  • Rapid prototyping of pathology AI models

  • Programmatic slide and report retrieval

  • Integration into machine learning pipelines

Scaling the Infrastructure for Computational Pathology

As digital pathology adoption accelerates, scalable and structured data access becomes critical. By continuously expanding the DataHub and maintaining structured report integration alongside WSIs, HistAI is building foundational infrastructure for the next generation of computational pathology systems.

The addition of 50,000+ new slides marks another step toward making large-scale, high-quality pathology datasets accessible to the broader research and AI community.