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TOPIC #411 - PHASE I SBIR CONTRACT - DE-IDENTIFICATION SOFTWARE TOOLS FOR CANCER IMAGING RESEARCH

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 75N91020C00023
Agency Tracking Number: 75N91020C00023-0-9999-0
Amount: $386,526.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: 411
Solicitation Number: N/A
Timeline
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
1274 PASADENA AVE NE
ATLANTA, GA 30306-3118
United States
DUNS: 117206504
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Paul Bunting
 (404) 314-9653
 paul@biodataconsortium.com
Business Contact
 Paul Bunting
Phone: (404) 314-9653
Email: paul@biodataconsortium.com
Research Institution
N/A
Abstract

Developing artificial intelligence technology for medical imaging applications requires training models on large and diverse datasets. Currently, aggregation of large data repositories, including radiology and pathology images, is limited by concerns around patient privacy. In order to successfully share medical images, an institution must be able to quickly and accurately de-identify large numbers of images in batches. This process is currently manual and time-consuming. We propose a pipeline to remove PHI from both radiology DICOM images and pathology whole slide images by leveraging machine learning, natural language processing, and compartmentalized workflow techniques to significantly reduce the human intervention needed to anonymize medical images. In addition to examining header data in the images, we will use optical character recognition and computer vision algorithms to detect text in any location or orientation in the image, then automatically record and subsequently purge these regions. These techniques will be configured to work on a variety of image types (CT, MRI, radiograph, etc) and cover multiple OEM vendors for both radiology and pathology images. This phase I statement of work will construct the software tools, methods, and datasets necessary to facilitate a phase II where the complex algorithms needed for autonomous deidentification will be developed. This phase II processing will be referred to throughout this document as the workflow.

* Information listed above is at the time of submission. *

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