How it works
Bioptimus models are pretrained on large, diverse histology data, so their representations transfer to many tasks without training from scratch. Point the SDK at a slide and a model, and it tiles the slide, masks out background, runs the model, and writes results to disk. For precise definitions, see the Glossary.- 1. Whole slide image
- 2. Tissue segmentation
- 3. Tile embeddings
- 4. Spatial gene expression
A scanned H&E slide can be billions of pixels — too large to process at once — so the pipeline starts from the slide and splits it into small tiles, each processed independently.
The models
H-Optimus
A vision foundation model for histology. Extracts tile-level features from H&E whole slide images.
M-Optimus
A multimodal, multi-scale model (M-Optimus-1) that predicts spatial gene expression from routine H&E, refined with bulk RNA.
STELA — data engine
A multi-institutional data engine generating the deeply profiled, clinically linked patient data our models train on.
Where to get each model
A quick map of which model is available on which channel, and where to start.AWS & SageMaker
Managed endpoints from AWS Marketplace.
On-premise
Self-hosted container for full data control.
Hugging Face
H-Optimus weights for non-commercial academic use.
Where to start
Run your first inference
Deploy a model and get embeddings back in minutes. For ML engineers and data scientists.
Explore use cases
See how teams use our models for biomarker discovery, indication expansion, and trial design.
Security & compliance
Data handling, residency, and deployment options for regulated environments.