A DICOM dataset for evaluation of medical image de-identification

Michael Rutherford; Seong K. Mun; Betty Levine; William Bennett; Kirk Smith; Phil Farmer; Quasar Jarosz; Ulrike Wagner; John Freyman; Geri Blake; Lawrence Tarbox; Keyvan Farahani; Fred Prior

doi:10.1038/s41597-021-00967-y

Scientific Data (Jul 2021)

A DICOM dataset for evaluation of medical image de-identification

Michael Rutherford,
Seong K. Mun,
Betty Levine,
William Bennett,
Kirk Smith,
Phil Farmer,
Quasar Jarosz,
Ulrike Wagner,
John Freyman,
Geri Blake,
Lawrence Tarbox,
Keyvan Farahani,
Fred Prior

Affiliations

Michael Rutherford: Department of Biomedical Informatics, University of Arkansas for Medical Sciences
Seong K. Mun: Arlington Innovation Center: Health Research, Virginia Tech
Betty Levine: Department of Biomedical Informatics, University of Arkansas for Medical Sciences
William Bennett: Department of Biomedical Informatics, University of Arkansas for Medical Sciences
Kirk Smith: Department of Biomedical Informatics, University of Arkansas for Medical Sciences
Phil Farmer: Department of Biomedical Informatics, University of Arkansas for Medical Sciences
Quasar Jarosz: Department of Biomedical Informatics, University of Arkansas for Medical Sciences
Ulrike Wagner: Frederick National Laboratory for Cancer Research
John Freyman: Frederick National Laboratory for Cancer Research
Geri Blake: Department of Biomedical Informatics, University of Arkansas for Medical Sciences
Lawrence Tarbox: Department of Biomedical Informatics, University of Arkansas for Medical Sciences
Keyvan Farahani: Center for Biomedical Informatics and Information Technology, National Cancer Institute
Fred Prior: Department of Biomedical Informatics, University of Arkansas for Medical Sciences

DOI: https://doi.org/10.1038/s41597-021-00967-y
Journal volume & issue: Vol. 8, no. 1
pp. 1 – 8

Abstract

Read online

Measurement(s) Deidentification • Clinical Data Technology Type(s) data synthesis • digital curation Factor Type(s) imaging type Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14802774

Published in Scientific Data

ISSN: 2052-4463 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/sdata/

About the journal