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.
Synthetic Chest X-Ray Image Generator
By:
Latest Version:
2.1
This solution generates synthetic Chest X-Ray images for educational and research purposes by leveraging SOTA generative AI techniques.
Product Overview
In medical domain, its often challenging to collect large and diverse dataset due to privacy concerns and limited access to patient data. Real medical images may contain sensitive patient information, and sharing or using such data for research can raise privacy and ethical concerns. Synthetic images offer a way to bypass these issues as they do not contain any real patient data. Some rare health disease or conditions may have limited available data. Synthetic data can be used to augment the existing dataset, increasing its size and diversity and balance class distribution. Given a reference image, this solution can generate upto 3 synthetic images in real-time inference and more than 3 using batch inference. The generated images show some variations in synthesis without deviating from the underlying representations it has learnt from the real chest Xray images.
Key Data
Version
By
Type
Model Package
Highlights
The solution can be used to demonstrate examples of several medical conditons which would facilitate research on new image analysis algorithms in medical domain.
This solution is trained on CheXpert dataset, which is a large dataset of Chest X-Rays. It uses deep learning based generative models that allows for the synthesis of realistic images which capture the underlying distribution of the training data.
Mphasis Synth Studio is an Enterprise Synthetic Data Platform for generating high-quality synthetic data that can help derive and monetize trustworthy business insights, while preserving privacy and protecting data subjects. Build reliable and high accuracy models when no or low data is available.
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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.
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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$5.00/inference
running on any instance
Model Batch Transform$10.00/hr
running on ml.p3.2xlarge
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$2.72/host/hr
running on ml.g4dn.8xlarge
SageMaker Batch Transform$3.825/host/hr
running on ml.p3.2xlarge
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
The input zip folder has a folder reference_image that has the reference image in it and a json file specifying the number of images to generate
Input MIME type
application/zipSample input data
Output
Summary
Output will be a zip folder containing all the generated images.
Output MIME type
application/zipSample output data
Sample notebook
Additional Resources
End User License Agreement
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Support Information
Synthetic Chest X-Ray Image Generator
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