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    Lymphoma Subtype Classification

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    Deployed on AWS
    Classifies non-Hodgkin’s Lymphoma subtypes

    Overview

    Non-Hodgkin lymphoma (also known as non-Hodgkin’s lymphoma, NHL, or sometimes just lymphoma) is a cancer that starts in white blood cells called lymphocytes, which are part of the body’s immune system. This model illustrates the potential of Deep Learning - in contributing to the automation of cancer diagnosis, by proposing a method to classify the subtypes of Lymphoma. This model is not intended for medical and diagnostic purpose.

    Highlights

    • Multi-class classifier model that classifies Lymphoma Images as Chronic Lymphocytic Leukaemia, Follicular and Mantle Cell Lymphoma
    • Trained using AlexNet (CNN Architecture)
    • Acknowledgement: Janowczyk A, Madabhushi A. Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. J Pathol Inform 2016;7:29. (http://www.andrewjanowczyk.com/use-case-7-lymphoma-sub-type-classification/)

    Details

    Delivery method

    Latest version

    Deployed on AWS

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    Pricing

    Lymphoma Subtype Classification

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    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.

    Usage costs (2)

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    Dimension
    Description
    Cost/host/hour
    ml.m4.xlarge Inference (Batch)
    Recommended
    Model inference on the ml.m4.xlarge instance type, batch mode
    $0.00
    ml.m4.xlarge Inference (Real-Time)
    Recommended
    Model inference on the ml.m4.xlarge instance type, real-time mode
    $0.00

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    Since you are not being charged currently for the use of this software there will be no refund of any charges.

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    Usage information

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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:
    Deploy the model as an API endpoint for your applications. When you send data to the endpoint, SageMaker processes it and returns results by API response. The endpoint runs continuously until you delete it. You're billed for software and SageMaker infrastructure costs while the endpoint runs. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Deploy models for real-time inference  .
    Deploy the model to process batches of data stored in Amazon Simple Storage Service (Amazon S3). SageMaker runs the job, processes your data, and returns results to Amazon S3. When complete, SageMaker stops the model. You're billed for software and SageMaker infrastructure costs only during the batch job. Duration depends on your model, instance type, and dataset size. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Batch transform for inference with Amazon SageMaker AI  .
    Version release notes

    First version released to AWS ML Marketplace

    Additional details

    Inputs

    Summary

    Download the Jupyter notebook in "Additional Resources" section and follow readme.txt provided.

    Input MIME type
    image/tiff
    See Input Summary
    See Input Summary

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