prov-gigapath/prov-gigapath

Prov-GigaPath: A whole-slide foundation model for digital pathology from real-world data

What it solves

Prov-GigaPath addresses the challenge of analyzing whole-slide images (WSI) in digital pathology. These images are typically too massive for standard AI models to process as a single input, requiring a way to extract meaningful features from both local tissue patterns and the overall slide-level context.

How it works

The system uses a two-stage encoding architecture:

  1. Tile Encoder: Extracts local patterns and features from individual image patches (tiles) of a slide.
  2. Slide Encoder: Takes the embeddings from the tile encoder along with their spatial coordinates to generate a comprehensive representation of the entire slide.

To improve efficiency, the project provides GigaPath-Flash, a lightweight variant that uses a LongNet backbone and a DINOv2-small tile encoder to reduce memory usage and increase inference speed.

Who it’s for

It is designed for AI researchers working on pathology foundation models, digital pathology, and the encoding of whole-slide image data.

Highlights

  • Foundation Model: A large-scale model trained on real-world digital pathology data.
  • Two-Tier Architecture: Separates local patch-level feature extraction from global slide-level aggregation.
  • Efficiency Variants: Includes "Flash" versions for faster inference and lower memory requirements.
  • Research-Ready: Provides pre-extracted embeddings for datasets like PCam and PANDA to facilitate fine-tuning experiments.

Related

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    mahmoodlab/TRIDENT

    A toolkit for large-scale whole-slide image processing in AI pathology, providing an end-to-end pipeline for tissue segmentation and feature extraction using numerous foundation models.

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    histolab/histolab

    A tool for processing Whole Slide Images (WSIs) in digital pathology, providing automatic tissue detection and informative tile extraction for deep learning pipelines.

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    rendeirolab/LazySlide

    A Python framework for whole slide image analysis that integrates with the scverse ecosystem to enable interoperable and scalable histological workflows.

  • Project
    prov-gigatime/GigaTIME

    A multimodal AI project that generates virtual spatial proteomics (mIF) profiles from routine H&E pathology slides for tumor microenvironment modeling.

  • Project
    Dana-Farber-AIOS/pathml

    PathML is an open‑source Python library (also available as a Docker image) that streamlines the whole‑slide pathology workflow: loading diverse slide formats, applying scalable preprocessing (stain normalisation, tiling, GPU transforms), converting data to PyTorch tensors, building cell‑level graphs, and running deep‑learning or ONNX models. It aims to lower the barrier to AI‑driven computational pathology and is already used in many high‑impact biomedical publications.