Sold by: MphasisÂ
Deployed on AWS
This solution generates synthetic Chest X-Ray images for educational and research purposes by leveraging SOTA generative AI techniques.
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.
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.
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost |
|---|---|---|
ml.p3.2xlarge Inference (Batch) Recommended | Model inference on the ml.p3.2xlarge instance type, batch mode | $10.00/host/hour |
ml.p3.8xlarge Inference (Batch) | Model inference on the ml.p3.8xlarge instance type, batch mode | $10.00/host/hour |
inference.count.m.i.c Inference Pricing | inference.count.m.i.c Inference Pricing | $5.00/request |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
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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
latest version
Additional details
Inputs
- 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/zip
https://github.com/Mphasis-ML-Marketplace/Synthetic-Chest-X-Ray-Image-Generator/tree/main/Input/user_input
https://github.com/Mphasis-ML-Marketplace/Synthetic-Chest-X-Ray-Image-Generator/tree/main/batch_input
Resources
Vendor resources
Support
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