# Prov-GigaPath (Microsoft, Providence)

Source: https://onco.cc/technologies/prov-gigapath/  
OnCo record `prov-gigapath` (Technology). Data CC BY-NC 4.0, attribute "Data from OnCo (onco.cc)"; commercial use needs a licence.

## TL;DR

An open pathology model trained on 1.3 billion image tiles from a US health system, modelling whole slides at gigapixel scale.

## Summary

Prov-GigaPath is an open pathology foundation model from Microsoft and Providence, published in Nature in 2024. It pairs a DINOv2 tile encoder with a slide-level LongNet encoder whose dilated attention can model an entire gigapixel slide, and it was trained on 1.3 billion image tiles from 171,189 slides drawn from more than 30,000 Providence patients. The weights are open, and the model performs strongly on mutation prediction and cancer subtyping benchmarks. Its limitations are that the training data come from a single US health system, which may limit generalisation to other scanners and populations, and that whole-slide attention is computationally heavy. It sits alongside UNI, CONCH and TITAN as one of the reference models of computational pathology. For a newcomer, Prov-GigaPath is a model that reads an entire slide at once rather than piece by piece.

## Fields

- Kind: Technology
- Status: emerging
- Last checked: 2026-09-08
- Tags: foundation-model; pathology
- Principle: Tile encoder (DINOv2) plus a slide-level LongNet encoder.
- Since: 2024
- Strengths: Open weights; Whole-slide context
- Limitations: Single health system source; Heavy compute

## Sources

- Nature 2024: https://doi.org/10.1038/s41586-024-07441-w

## Connected records

- fronts: [AI & Computation](https://onco.cc/fronts/ai-computation/)
- technologies: [Pathology & radiology foundation models](https://onco.cc/technologies/pathology-foundation-model/)
- companies: [Microsoft (Research and Health AI)](https://onco.cc/companies/microsoft-research/)
- institutions: [Providence Health & Services](https://onco.cc/institutions/providence-health/)
- key papers: [A whole-slide foundation model for digital pathology from real-world data](https://onco.cc/key-papers/paper-xu-nature/)
- roadmaps: [AI in oncology roadmap: pattern readers → foundation models → agents in the workflow](https://onco.cc/roadmaps/ai-oncology-roadmap/), [AI in the oncology clinic: from narrow cleared tools to multimodal decision support](https://onco.cc/roadmaps/ai-oncology-clinic/), [Diagnostics roadmap: stains → gene panels → blood tests that decide treatment](https://onco.cc/roadmaps/diagnostics-roadmap/)

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