Sold by: Bioptimus
Deployed on AWS
Multimodal foundation model for oncology. M-Optimus-1 turns a routine H&E slide into ~6,000-gene spatial expression readouts. Pre-trained on millions of proprietary H&E whole-slide images (50+ tissues) plus paired patient cohorts with aligned H&E, bulk RNA-seq, and spatial transcriptomics.
Overview
Use M-Optimus-1 to build oncology models from H&E whole-slide images, optionally paired with bulk RNA-seq. Generate multimodal embeddings and reconstruct spatial gene expression across a curated panel of 6,000+ genes.
M-Optimus-1 is a multimodal foundation model for oncology, learning cross-modal representations across H&E, bulk RNA-seq, and spatial transcriptomics. Pre-trained using a unique proprietary dataset comprising millions of H&E whole slide images from more than 50 organ tissues, and thousands of patient records where H&E, bulk and spatial transcriptomics are paired and aligned.
Deploy M-Optimus-1 as an Amazon SageMaker model package inside your AWS account. Run real-time inference via SageMaker endpoints or batch inference on S3. Your data stays private, and is never accessed by Bioptimus.
Highlights
- M-Optimus-1 is a multimodal, multiscale foundation model for oncology that integrates information across H&E, bulk RNA-seq and spatial transcriptomics (10x Genomics Visium) to produce tile-, slide, and patient-level outputs.
- M-Optimus-1 can perform direct prediction from H&E (with optional bulk RNA-seq) to spatial expression across a curated pan-tissue panel of over 6,000 genes - tumor and immune cell biomarkers, cell-cell interactions, ligands and receptors, biological pathways, and drug candidates.
- M-Optimus-1 is multimodal by design, flexible in deployment. H&E-only inference still outperforms unimodal baselines when sequencing data is unavailable.
Details
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Dimension | Description | Cost |
|---|---|---|
ml.g5.xlarge Inference (Batch) Recommended | Model inference on the ml.g5.xlarge instance type, batch mode | $3,000.00/host/hour |
ml.g5.2xlarge Inference (Batch) | Model inference on the ml.g5.2xlarge instance type, batch mode | $3,000.00/host/hour |
inference.count.m.i.c Inference Pricing | inference.count.m.i.c Inference Pricing | $0.01/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
v1.0.1
- Minor stability fixes
Additional details
Inputs
- Summary
Required: H&E whole-slide images at 224×224 px tiles, 0.5 MPP. Optional: H&E tiles at 512x512 px 8 MPP for tissue segmentation model.
Optional: paired bulk RNA-seq (.csv / .tsv / .txt / .h5ad). Exact Ensembl IDs match to the supported Ensembl ID list (see sample input data for details). All inputs should be base64 encoded json files. Inputs can contain multiple tiles at a time (jsonl format), however the total file size should not exceed 100MB for batch jobs and 6MB for real-time jobs.
- Limitations for input type
- Inputs must be in the specified json format with base64 encoded images. Please see sample input files for details. **Input MIME type:** application/json
- Input MIME type
- application/json
https://github.com/bioptimus/m-jumpstart/tree/main/data/input/real-time
https://github.com/bioptimus/m-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 |
|---|---|---|---|
image_data | Base64-encoded PNG of the tile | Type: string | Yes |
slide_name | Identifier for the source slide | Type: string | Yes |
x | Tile x-coordinate in the slide | Type: int | Yes |
y | Tile y-coordinate in the slide | Type: int | Yes |
width | Tile width in pixels | Type: int | Yes |
height | Tile height in pixels | Type: int | Yes |
patch_idx | Index of this tile | Type: int | Yes |
model_name | Name of the model must be "m-optimus" | Type: string | Yes |
mode | "prediction" (for gene expression) or "embedding" | Type: string | Yes |
bulk_rna | Bulk RNA counts vector. If omitted, the server substitutes zeros. Must be in the exact gene order from the model's input_gene_set.csv. Pre-processed as log1p before passing to the model - so this should be raw counts. | Type: list[float] | No |
Resources
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Support
Vendor support
Contact: support@bioptimus.com
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