Sold by: John Snow Labs
Deployed on AWS
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Automated PHI redaction in DICOM images, preserving data integrity while ensuring compliance with healthcare privacy regulations.
Overview
This pipeline enables automated PHI redaction in DICOM images, ensuring compliance with HIPAA and other healthcare privacy regulations. The model performs the least intrusive form of DICOM de-identification removing only the most critical PHI from images and most essential metadata fields while preserving all non-sensitive details for research and analysis. The output is a de-identified DICOM document with blacked-out sensitive data, making it suitable for secure sharing, AI training, and regulatory compliance.
Key applications includes: Automated PHI Redaction - Seamlessly integrate into hospital imaging systems; Secure Image Sharing - Enable safe collaboration across medical institutions; Compliance & Auditability - Maintain traceable PHI removal for regulatory adherence.
With this solution, data scientists and healthcare providers can confidently process medical images while safeguarding patient privacy and research integrity.
Build based on Healthcare NLP 6.4.0 version - https://nlp.johnsnowlabs.com/docs/en/jsl/jsl-release-notes#section-2
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:** * 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** * [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/)
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Pricing
Free trial
Try this product free for 14 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 that runs the de-identification model. Two options are available, split by processing mode. The ml.m4.2xlarge instance runs batch inference, which handles images in grouped jobs. The ml.m4.xlarge instance runs real-time inference, which processes images as they arrive. Each instance type carries its own hourly rate, so your cost depends on which instance and mode you choose and how many hours you run it. There is no upfront commitment; you are charged for actual host hours used.
Top-of-mind questions for buyers
Each dimension maps to one running AWS instance of that type. The ml.m4.2xlarge carries more vCPU and memory than the ml.m4.xlarge. You pay the hourly rate tied to whichever instance type runs your de-identification job, counted per host hour.
The software charge meters running host hours only. A stopped instance does not accrue software charges. Underlying AWS infrastructure fees, such as storage, may still apply while the instance is paused. Charges resume once the instance runs again.
Batch inference on the ml.m4.2xlarge processes images in grouped jobs, so you run the instance for the duration of each job. Real-time inference on the ml.m4.xlarge processes images as they arrive. Both meter actual host hours. Batch suits scheduled volume; real-time suits on-demand requests.
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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
Mask PHI information in DICOM images, removing the least possible amount of tags and image texts.
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_minimal/inputs/real-time
https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/products/sagemaker/models/dicom_deid_generic_augmented_minimal/inputs/batch
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Support
Vendor support
For any assistance, please reach out to support@johnsnowlabs.com .
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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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