Sold by: Bioptimus
Deployed on AWS
The leading foundation model for computational pathology. H-optimus-1 turns H&E whole-slide images into task-agnostic embeddings for biomarker discovery, mutation prediction, and survival modeling, with state-of-the-art performance on 13 downstream tasks. 1.1B-parameter Vision Transformer pre-trained on 1M+ slides from 800,000+ patients across 50+ organ systems.
Overview
Use H-optimus-1 to build pathology models from H&E whole-slide images. Generate task-agnostic embeddings that preserve cellular and tissue-level context, then plug them into classifiers, survival models, or downstream heads for biomarker discovery, mutation prediction, patient stratification, tissue classification, cell counting, typing, and segmentation. Zero-shot and few-shot adaptation lets you fit a new task with linear probing or light fine-tuning instead of a full training run.
H-optimus-1 is a 1.1B-parameter Vision Transformer pre-trained on over 1 million H&E slides from more than 800,000 patients across 4,000+ clinical centers and 50+ organ systems (healthy and diseased tissues). On internal and public benchmarks, it delivers state-of-the-art performance across 13 downstream tasks on 15 datasets, including the public HEST benchmark.
Deploy H-optimus-1 as an Amazon SageMaker model package inside your AWS account. Run real-time inference via SageMaker endpoints or batch inference on S3. Whole-slide images stay in your VPC.
Highlights
- H-optimus-1 delivers state-of-the-art performance across 13 downstream tasks on 15 public and private datasets, including the public HEST benchmark; supports zero-shot and few-shot adaptation via linear probing or light fine-tuning with minimal task-specific training.
- H-optimus-1 is pre-trained on a proprietary cohort of billions of image patches sampled from over 1 million H&E slides representing more than 800,000 patients across 4,000+ clinical centers and 50+ organ systems, spanning healthy and diseased tissues.
- H-optimus-1 is a 1.1B-parameter Vision Transformer for H&E whole-slide images pre-trained with self-supervised learning at 0.5 MPP and 224x224 px tile resolution, producing 1,536-d task-agnostic embeddings that preserve cellular and tissue-level context.
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Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost |
|---|---|---|
ml.g5.xlarge Inference (Batch) Recommended | Model inference on the ml.g5.xlarge instance type, batch mode | $600.00/host/hour |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $600.00/host/hour |
inference.count.m.i.c Inference Pricing | inference.count.m.i.c Inference Pricing | $0.002/request |
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Initial version
Additional details
Inputs
- Summary
H-optimus-1 expects as input 224x224 pixels histology tile images of 0.5 microns per pixel resolution.
- Limitations for input type
- H-optimus-1 is expected to perform less well on images at other resolutions (e.g. 0.25 or 1.0 MPP).
- Input MIME type
- image/*, application/x-image
https://github.com/bioptimus/h1-jumpstart/blob/main/data/input/real-time/example_input.png
https://github.com/bioptimus/h1-jumpstart/tree/main/data/input/batch
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