Sold by: Mphasis
Deployed on AWS
This solution analyzes images of human chest X-Rays and predicts whether the patient has a Pneumothorax (collapsed lung) or not.
Overview
This is a hybrid classical-quantum machine learning based solution which detects Pneumothorax from chest x-ray images. This solution adopts Quanvolutional Neural Network (QNN) to extract useful features in the data for classification purposes. A variational circuit with an optimizable parameter transforms the state producing feature maps as output. The quanvolutional model in this solution does not use a trainable classical layer, making this a pure quantum solution. The algorithm used in this solution inherits variational quantum circuit layers with trained parameters dedicated for x-ray image classification.
Highlights
- Pneumothorax can be caused by a blunt chest injury, damage from underlying lung disease, or it may occur for no obvious reason at all. On some occasions, a collapsed lung can be a life-threatening event. Pneumothorax is usually diagnosed by a radiologist on a chest x-ray, and can sometimes be very difficult to confirm. An accurate algorithm to detect pneumothorax would be useful in a lot of clinical scenarios. This solution could be used to triage chest radiographs for priority interpretation, or to provide a more confident diagnosis for non-radiologists.
- Quantum based Pneumothorax detection solution analyzes the images of x-ray and predicts presence or absence of Pneumothorax. The current solution provides quantum ML based alternative to state of the art classifical deep learning based image classification systems.
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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/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $40.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $20.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $40.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $40.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $40.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $40.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $40.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $40.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $40.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $40.00 |
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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
This is version 1.5. Bug Fixes.
Additional details
Inputs
- Summary
- The input dataset should be a zip file containing a folder of images in png format.
- Input zip folder should not contain more than 10 images.
- Input MIME type
- application/zip, text/csv, text/plain
https://github.com/Mphasis-ML-Marketplace/QML-based-pneumothorax-detection/blob/main/input.zip
https://github.com/Mphasis-ML-Marketplace/QML-based-pneumothorax-detection/blob/main/data/input/batch/input.zip
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