Heather M. Whitney, PhD is an assistant professor in the Department of Radiology at the University of Chicago. Dr. Whitney received a Master of Science in Medical Physics from the Vanderbilt University School of Medicine and Master of Science and PhD in Physics from Vanderbilt University. While at Vanderbilt, she trained and conducted research at the Vanderbilt University Institute of Imaging Science with John Gore as her advisor, and additionally collaborated with faculty in the Department of Radiation Oncology. Before coming to the University of Chicago, she was a tenured professor of physics at a small liberal arts college, where she fostered an NIH-funded research program in medical physics in collaboration with faculty in Radiology at the University of Chicago.
At the University of Chicago, she conducts research in primarily in computer-aided diagnosis of breast and ovarian cancer, focusing on the modalities of dynamic contrast-enhanced magnetic resonance imaging and ultrasound. Her overall areas of interest are in artificial intelligence and radiomics across the imaging and classification pipeline, from image acquisition to performance evaluation and data harmonization. She also conducts research and collaborates in MIDRC, the Medical Imaging and Data Resource Center. Within MIDRC she works on methods of task-based distributions, interoperability between data enclaves, and monitoring and studying the representativeness of the MIDRC data commons to foster research in AI.
Dr. Whitney also collaborates with UChicago faculty on applications of imaging science to gynecology, ophthalmology, and hematology/oncology.
University of Chicago
Chicago, IL
- Clinical Medical Ethics Fellowship
2024
Vanderbilt University
Nashville, TN
PhD - Physics
2009
Vanderbilt University School of Medicine
Nashville, TN
MS - Medical Physics
2007
Vanderbilt University
Nashville, TN
MS - Physics
2006
King College
Bristol, TN
BS - Physics and Performing & Visual Arts
2003
Introduction to the JMI Special Issue on Advances in Breast Imaging.
Introduction to the JMI Special Issue on Advances in Breast Imaging. J Med Imaging (Bellingham). 2025 Nov; 12(Suppl 2):S22001.
PMID: 40937191
Demonstration of Interoperability Between MIDRC and N3C: A COVID-19 Severity Prediction Use Case.
Demonstration of Interoperability Between MIDRC and N3C: A COVID-19 Severity Prediction Use Case. J Imaging Inform Med. 2025 Aug 14.
PMID: 40813937
Multimodal data curation via interoperability: use cases with the Medical Imaging and Data Resource Center.
Multimodal data curation via interoperability: use cases with the Medical Imaging and Data Resource Center. Sci Data. 2025 Aug 01; 12(1):1340.
PMID: 40750795
Hybrid artificial intelligence echogenic components-based diagnosis of adnexal masses on ultrasound.
Hybrid artificial intelligence echogenic components-based diagnosis of adnexal masses on ultrasound. Med Phys. 2025 Jul; 52(7):e17983.
PMID: 40665507
Sureness of classification of breast cancers as pure ductal carcinoma in situ or with invasive components on dynamic contrast-enhanced magnetic resonance imaging: application of likelihood assurance metrics for computer-aided diagnosis.
Sureness of classification of breast cancers as pure ductal carcinoma in situ or with invasive components on dynamic contrast-enhanced magnetic resonance imaging: application of likelihood assurance metrics for computer-aided diagnosis. J Med Imaging (Bellingham). 2025 Nov; 12(Suppl 2):S22012.
PMID: 40538452
MIDRC mRALE Mastermind Grand Challenge: AI to predict COVID severity on chest radiographs.
MIDRC mRALE Mastermind Grand Challenge: AI to predict COVID severity on chest radiographs. J Med Imaging (Bellingham). 2025 Mar; 12(2):024505.
PMID: 40276098
Hybrid artificial intelligence echogenic components-based diagnosis of adnexal masses on ultrasound.
Hybrid artificial intelligence echogenic components-based diagnosis of adnexal masses on ultrasound. ArXiv. 2025 Apr 16.
PMID: 40321943
AI analysis of medical images at scale as a health disparities probe: a feasibility demonstration using chest radiographs.
AI analysis of medical images at scale as a health disparities probe: a feasibility demonstration using chest radiographs. ArXiv. 2025 Apr 08.
PMID: 40297238
Impact of retraining and data partitions on the generalizability of a deep learning model in the task of COVID-19 classification on chest radiographs.
Impact of retraining and data partitions on the generalizability of a deep learning model in the task of COVID-19 classification on chest radiographs. J Med Imaging (Bellingham). 2024 Nov; 11(6):064503.
PMID: 39734609
AI-based automated segmentation for ovarian/adnexal masses and their internal components on ultrasound imaging.
AI-based automated segmentation for ovarian/adnexal masses and their internal components on ultrasound imaging. J Med Imaging (Bellingham). 2024 Jul; 11(4):044505.
PMID: 39114540
Fellow
American Association of Physicists in Medicine
2026
Emerging Cancer Scholars Exchange
2025
Scialog Fellow in Advancing Bioimaging
Research Corporation
2023
Scialog Fellow in Advancing Bioimaging
Research Corporation
2022
Community Champion
SPIE
2020
Junior Faculty Achievement Award
Wheaton College
2015
Young Alumni Achievement Award
King University
2014
Sigma Pi Sigma
Physics Honors Society
2013
Natural Sciences and Mathematics Top Graduate Award
King College
2003
Arthur W. King Memorial Scholarship in Physics
King College
2002
Distinguished Scholar
Appalachian College Association
2001