Sold by: John Snow Labs
Deployed on AWS
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Replace values in metadata with pseudonyms, and most of the tags in the metadata of the DICOM image analyzed.
Overview
This pipeline offers a cutting-edge solution for healthcare data scientists focused on data privacy and adherence to health regulations, effectively masking PHI information in DICOM images, by replacing personal identifiers with pseudonyms instead of removing them, ensuring that PHI is no longer traceable while maintaining data integrity for longitudinal studies and collaborations. It effectively masks PHI information by adding black bounding boxes to conceal PHI while preserving the original file structure for secure sharing and collaborative efforts. Key features include Automated PHI Redaction - integrates with hospital systems for immediate PHI detection and masking; Secure Image Sharing, allowing distribution of de-identified images; Compliance and Audit Trails - align with HIPAA and other regulations. This tool is crucial for healthcare organizations prioritizing data privacy in medical research and collaborations, guaranteeing security and quality of medical imaging data.
IMPORTANT USAGE INFORMATION
After subscribing to this product and creating a SageMaker endpoint, billing occurs on an HOURLY BASIS for as long as the endpoint is running.
-Charges apply even if the endpoint is idle and not actively processing requests.
-To stop charges, you MUST DELETE the endpoint in your SageMaker console.
-Simply stopping requests will NOT stop billing.
This ensures you are only billed for the time you actively use the service.
Highlights
- **Benefits:** * Facilitates Research and Development: Securely de-identify PHI in DICOM images enabling researchers to use real patient data without compromising privacy, accelerating medical research of new treatments and technologies. * Enhanced Patient Trust and Confidentiality: healthcare organizations can improve trust with their patients * Reduced Risk of Data Breaches: Automating the process of de-identifying sensitive information, the risk of data breaches is significantly reduced.
- **Additional resources** * [DICOM de-identification part 1](https://www.johnsnowlabs.com/dicom-de-identification-at-scale-in-visual-nlp-1-3/) * [DICOM de-identification part 2](https://www.johnsnowlabs.com/dicom-de-identification-at-scale-in-visual-nlp-2-3/) * [DICOM de-identification part 3](https://www.johnsnowlabs.com/dicom-de-identification-at-scale-in-visual-nlp-3-3/)
Details
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Pricing
Free trial
Try this product free for 15 days according to the free trial terms set by the vendor.
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.m4.2xlarge Inference (Batch) Recommended | Model inference on the ml.m4.2xlarge instance type, batch mode | $47.52 |
ml.m4.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m4.xlarge instance type, real-time mode | $23.76 |
Dimensions summary
You pay by the hour based on the AWS instance you run and the processing mode you choose. One option covers batch inference on the ml.m4.2xlarge instance, which processes DICOM images in groups. The other covers real-time inference on the ml.m4.xlarge instance, which handles requests as they arrive. These are two separate usage-based options, not tiers. Your cost scales with how many host hours each instance runs. You select the instance and mode that fit your workload, and charges appear on your AWS bill.
Top-of-mind questions for buyers
One host hour is one hour that the chosen instance runs the model. Charges accrue only while the instance is active and processing. A stopped or powered-off instance does not accrue software charges. Underlying AWS resource fees may still apply separately for storage or other running services.
Both meter by host hours on their respective instances. Batch inference runs on the ml.m4.2xlarge and processes DICOM images in groups, so hours accrue during scheduled runs. Real-time inference runs on the ml.m4.xlarge and handles requests as they arrive, so hours accrue while the endpoint stays available.
No document or image count limit applies. You pay for host hours the instance runs, not per image. Throughput depends on your instance size and image volume. Choose batch mode for grouped processing or real-time mode for on-demand requests to match your workload.
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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
Upgrade underlying JSL libraries to version 6.4.0
Additional details
Inputs
- Summary
Supported Dicom input format.
- Input MIME type
- application/octet-stream
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/dicom_deid_generic_augmented_pseudonym/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/dicom_deid_generic_augmented_pseudonym/inputs/batch
Resources
Vendor resources
Support
Vendor support
For any assistance, please reach out to support@johnsnowlabs.com .
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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