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Prov-GigaPath (Microsoft, Providence)

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

Published in Nature in 2024, Prov-GigaPath was trained on 171,189 slides from 30,000+ Providence patients; uses LongNet dilated attention to model entire slides. Open weights; strong on mutation prediction and cancer subtyping.

Generic schematic · not to scale · placeholder for the ai computation front
Flagged finding · Neural network

How it works

Tile encoder (DINOv2) plus a slide-level LongNet encoder.

Strengths
  • Open weights
  • Whole-slide context
Limitations
  • Single health system source
  • Heavy compute
Since
2024

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