There seems to be a scarcity of scientifically-valid yet synthetic medical images.
We're exploring the business value of building synthetic, unencumbered Electronic Data Capture ("EDC") datasets at scale.
The initial phases of this project are:
- Identify the 05 most common disease targets for clinical trials that use imaging.
- Generate synthetic images (both DICOM as well as JPEG) for those disease targets.
- Integrate the SynthImaging output files with our current SynthSubmission work.
There are massive amounts of freely-available, unencumbered real imaging data:
- University of South Florida's "Digital Database for Screening Mammography"
- The Cancer Imaging Archive (TCIA)
Our plan is to leverage that real imaging data to then programmatically generate synthetic imaging files.
Please contact José if your organization would like to support this work.
NIHPO has consciously decided to publish its software's source code under the GNU AGPL license from the Free Software Foundation.
At NIHPO, we define ourselves as Data Artistes. We love Open Data, and our passion is turning Open Data into actionable knowledge.
Like artists everywhere, the greatest impediment to our success is when nobody knows about our art. We want every person who could benefit from our software to be free to try the software, in their own terms, with neither financial nor legal barriers.
We believe that by demonstrating both our art and our passion for this craft, upfront and with no restrictions, we'll be able to attract subscribers willing to pay us to enhance and customize the software we're providing here for free.
We're following the Product-led Growth strategy. We believe that the quality of our product, and that you can try it before you buy it, will open doors for us.
Please note that this software is licensed under the GNU AGPL.
Contact NIHPO for a commercial license, or if you're interested in licensing a customized version of the NIHPO Synthetic Health Data Platform.
©️ 2007-2021 NIHPO, Inc. Version 07 August 2021.