Sold by: VITech LabÂ
Deployed on AWS
Image analysis model that identifies anomalies in images and classifies them by severity to scale screening for diabetic retinopathy
Overview
The Diabetic retinopathy detector is an image analysis and anomaly detection model that identifies and classifies diabetic anomalies in eye screens. It scales eye screening and helps doctors detect signs of diabetic retinopathy. As all screens are ranked by severity, doctors can address urgent cases first, to diagnose faster and prevent sight loss. NB: The model is not FDA-compliant; for auxiliary/support use only.
We provide free support during the trial period! After you've succeeded with the subscription, reach out at support@vitechlab.comÂ
Highlights
- Automatically screen images for eye problems caused by diabetes; make eye screening more accessible to the populace through faster, more efficient diagnosis and reduced triage/triage costs
- The model is trained on images annotated by highly trained ophthalmologists; each image in the dataset was reviewed and the specific diagnosis was provided, thus ensuring high accuracy of model performance
- Need a custom-made solution for disease screening? Reach us at support@vitechlab.com
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.c4.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.c4.2xlarge instance type, real-time mode | $0.00 |
ml.c4.2xlarge Inference (Batch) Recommended | Model inference on the ml.c4.2xlarge instance type, batch mode | $0.00 |
ml.m4.4xlarge Inference (Real-Time) | Model inference on the ml.m4.4xlarge instance type, real-time mode | $0.00 |
ml.m5.4xlarge Inference (Real-Time) | Model inference on the ml.m5.4xlarge instance type, real-time mode | $0.00 |
ml.m5d.24xlarge Inference (Real-Time) | Model inference on the ml.m5d.24xlarge instance type, real-time mode | $0.00 |
ml.m4.16xlarge Inference (Real-Time) | Model inference on the ml.m4.16xlarge instance type, real-time mode | $0.00 |
ml.m5.2xlarge Inference (Real-Time) | Model inference on the ml.m5.2xlarge instance type, real-time mode | $0.00 |
ml.p3.16xlarge Inference (Real-Time) | Model inference on the ml.p3.16xlarge instance type, real-time mode | $0.00 |
ml.c5d.4xlarge Inference (Real-Time) | Model inference on the ml.c5d.4xlarge instance type, real-time mode | $0.00 |
ml.m4.2xlarge Inference (Real-Time) | Model inference on the ml.m4.2xlarge instance type, real-time mode | $0.00 |
Vendor refund policy
This product is offered for free. If there are any questions, please contact us for further clarifications.
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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
Version 1.0 released
Additional details
Inputs
- Summary
Example notebooks for deployment, Real Time inference and Batch Transformation
You can find all the information related to the usage of our product here: https://github.com/VITechLab/aws-sagemaker-examples/blob/master/Diabetic-Retinopathy-Detector/Â
It contains example Jupyter Notebooks showing how to deploy the model, run Real Time inference, run Batch Transform job to perform the inference on the data stored in Amazon S3 bucket. It also contains input and output data samples. As well as the code for visualizing the prediction results.
Using our model for real time prediction using python is as simple as this:
predictor = sagemaker.predictor.RealTimePredictor( ' your endpoint name ', sagemaker_session=sagemaker.Session(), content_type="image/jpeg" ) with open('data/sample_image.jpg', 'rb') as img: img_bytes = bytearray(img.read()) result = predictor.predict(img_bytes).decode("utf-8")Supported content types are [“image/jpeg”] Supported response types are “application/json”
You can find more details here: https://github.com/VITechLab/aws-sagemaker-examples/blob/master/Diabetic-Retinopathy-Detector/Â
- Input MIME type
- image/jpeg
See Input Summary
See Input Summary
Resources
Support
Vendor support
If you have any issues or feature requests, please write to us, and we will be happy to help you as soon as possible. We can also create custom software and models optimised for your specific use case. Reach us at support@vitechlab.comÂ
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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