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Overview
- Detects body parts for CT scans and completeness for chest abdomen pelvic region.
- See https://medical-ai.sawtellabs.com for more info.
Highlights
- CAUTION -- For Investigational Use Only. The performance characteristics of this product have not been established
- API returns height of each detected body part in mm: head, neck (including shoulder), chest, abdomen, pelvis, lower_limb.
- API also returns anatomy completeness for chest, abdomen and pelvis - i.e. whether or not the scan contains the full chest, abdomen and pelvic regions.
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
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Dimension | Description | Cost |
|---|---|---|
ml.m5.4xlarge Inference (Batch) Recommended | Model inference on the ml.m5.4xlarge instance type, batch mode | $30.00/host/hour |
ml.p2.xlarge Inference (Batch) | Model inference on the ml.p2.xlarge instance type, batch mode | $30.00/host/hour |
ml.m5.12xlarge Inference (Batch) | Model inference on the ml.m5.12xlarge instance type, batch mode | $30.00/host/hour |
ml.p2.16xlarge Inference (Batch) | Model inference on the ml.p2.16xlarge instance type, batch mode | $30.00/host/hour |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $30.00/host/hour |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $30.00/host/hour |
ml.m5.xlarge Inference (Batch) | Model inference on the ml.m5.xlarge instance type, batch mode | $30.00/host/hour |
ml.c5.9xlarge Inference (Batch) | Model inference on the ml.c5.9xlarge instance type, batch mode | $30.00/host/hour |
ml.c5.4xlarge Inference (Batch) | Model inference on the ml.c5.4xlarge instance type, batch mode | $30.00/host/hour |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $30.00/host/hour |
Vendor refund policy
Non-refundable.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
release to aws
Additional details
Inputs
- Summary
The input required by the model is a 3d Computed Tomography image. A custom processing step is required to pass this 3D image to the model endpoint, see "Input data descriptions" and the example Jupyter notebook for more detail on how to perform inference.
- Limitations for input type
- The input request body size limit configured for this model package via nginx is unlimited, however Amazon may have other constraints on the request body size.
- Input MIME type
- application/jsonlines, application/json
https://raw.githubusercontent.com/sawtellellc/apis/main/ct-scan-body-part-detector/sample-input/payload.txt
https://raw.githubusercontent.com/sawtellellc/apis/main/ct-scan-body-part-detector/sample-input/niigz-jsonlines.txt
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
application/json | The input data is a json object {"niigz":"${BASE64_ENCODED_NIIGZ}"}, where ${BASE64_ENCODED_NIIGZ} is a b64 encoded string converted from 3D image stored in a nii.gz format. | Type: FreeText | Yes |
Resources
Vendor resources
Support
Vendor support
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