Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

H-Optimus-0
By:
Latest Version:
1.0.0
Open-source AI foundation model for histopathology image analysis
Product Overview
H-optimus-0 is an open-source foundation model for histology, developed by Bioptimus. The model is a 1.1B parameter vision transformer trained on a proprietary collection of more than 500,000 H&E stained whole slide histology images. For more information, please refer to our GitHub repository here: https://github.com/bioptimus/releases/tree/main/models/h-optimus/v0?utm_source=owkin&utm_medium=referral&utm_campaign=h-bioptimus-o . H-optimus-0 can extract powerful features from histology images for various downstream applications, such as mutation prediction, survival analysis, or tissue classification.
Key Data
Version
Type
Model Package
Highlights
1.1B parameter Vision Transformer trained on a proprietary dataset of 500,000+ H&E stained whole slide histology images.
Currently (12/17/24) ranked #1 on the HEST benchmark: https://github.com/mahmoodlab/HEST?tab=readme-ov-file#hest-benchmark-results-083024
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$0.00/inference
running on any instance
Model Batch Transform$0.00/hr
running on ml.g5.xlarge
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$1.408/host/hr
running on ml.g5.xlarge
SageMaker Batch Transform$1.408/host/hr
running on ml.g5.xlarge
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on the number of inferences generated by the ML Model per month. Typically, the number of inferences is the same as the number of successful calls to the real-time endpoint. For models that support multiple inputs in a request, sellers have the option to meter the number of inputs processed in a request to count generated inferences.
Additional infrastructure cost, taxes or fees may apply.
Usage Information
Model input and output details
Input
Summary
H-optimus-0 expects a 224x224 pixels histology image as input at 0.5 microns per pixel resolution (MPP).
Limitations for input type
H-optimus-0 is expected to perform less well on images at other resolutions (e.g. 0.25 or 1.0 MPP).
Input MIME type
application/x-imageSample input data
Output
Summary
The model outputs a feature vector of dimension 1536. These features can be used for downstream applications such as tissue type prediction or mutation prediction.
Output MIME type
application/jsonSample output data
Sample notebook
Additional Resources
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Support Information
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