Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

DICOM Images De-identification - Full
By:
Latest Version:
5.5.0
Automate PHI redaction in DICOM images for secure data handling.
Product Overview
This advanced pipeline eliminates all visible text within DICOM images and removes or anonymizes most metadata fields, including patient identifiers, physician details, and hospital information. It is engineered for healthcare data scientists while ensuring data privacy and regulatory compliance. It seamlessly masks PHI within DICOM images, securing sensitive metadata and embedded texts, maintaining the original DICOM structure while overlaying black boxes over PHI entities and de-identifying metadata. Key Features include: Automated PHI Redaction -Integrates with hospital imaging systems to automatically obscure PHI; Secure Image Sharing - Enables safe distribution of de-identified images across healthcare facilities. Compliance and Audit Trails -Meets HIPAA standards with a traceable process for PHI removal and comprehensive audit logs. Essential for healthcare entities, this tool supports high privacy standards, enhancing medical research without compromising data quality.
Key Data
Version
Type
Model Package
Highlights
Benefits:
🔷 Precision Masking: Accurately identifies and obscures PHI within images to maintain patient confidentiality.
🔷Enhanced Data Security: Implements robust measures to ensure data remains protected both in transit and at rest.
🔷 Efficient Processing: Utilizes GPU resources for quick processing of large image files, reducing wait times significantly.
Additional resources
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
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Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$23.76/hr
running on ml.m4.xlarge
Model Batch Transform$47.52/hr
running on ml.m4.2xlarge
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$0.24/host/hr
running on ml.m4.xlarge
SageMaker Batch Transform$0.48/host/hr
running on ml.m4.2xlarge
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Realtime Inference/hr | |
---|---|---|
ml.m4.xlarge Vendor Recommended | $23.76 |
Usage Information
Sample notebook
Additional Resources
End User License Agreement
By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)
Support Information
DICOM Images De-identification - Full
For any assistance, please reach out to support@johnsnowlabs.com. https://spark-nlp.slack.com/archives/C06HG18DDDH
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