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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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 | $0.001/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
v2 Additional Features:
- Added Tissue Segmentation model which takes 512x512 tiles as input and outputs a tissue mask
- Added batch scheduler and configuration options for batch size and max wait time to optimise batch transform runs
- Added support for bioptimus client SDK
Additional details
Inputs
- Summary
H-optimus is a Foundation Model which accepts 224x224 pixel images at 0.5 MPP resolution and outputs an embedding feature vector of dimension 1536. The model accepts a single JSON object per request
- Limitations for input type
- Please note the input resolution should be 0.5 MPP (microns-per-pixel). Lower resolutions work less well.
- Input MIME type
- application/json
https://github.com/bioptimus/h1-jumpstart/tree/main/data/input/real-time
https://github.com/bioptimus/h1-jumpstart/tree/main/data/input/batch
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
model_name | Selects the model to run. Must be "h1". | String | Yes |
mode | Inference mode. Must be "embedding". | String | Yes |
image_data | Base64-encoded PNG of a 224×224 RGB tissue tile extracted at 0.5 µm/px (MPP). | String (base64) | Yes |
slide_name | Identifier for the source whole-slide image. | String | Yes |
x | Left edge of the tile within the source slide, in pixels. | integer ≥ 0 | Yes |
y | Top edge of the tile within the source slide, in pixels. | integer ≥ 0 | Yes |
width | Tile width in pixels. Must be 224. | integer = 224 | Yes |
height | Tile height in pixels. Must be 224. | integer = 224 | Yes |
patch_idx | Zero-based index of the tile within the slide. | integer ≥ 0 | Yes |
tissue_ratio | Fraction of the tile area containing tissue (0.0–1.0). | float [0.0, 1.0] | No |
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