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computer-aided diagnosis, machine learning, breast cancer, deep learning, radiomics, COVID-19
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Maryellen L. Giger, Ph.D. is the A.N. Pritzker Distinguished Service Professor of Radiology, Committee on Medical Physics, and the College at the University of Chicago. She is also the Vice-Chair of Radiology (Basic Science Research) and the immediate past Director of the CAMPEP-accredited Graduate Programs in Medical Physics/ Chair of the Committee on Medical Physics at the University.
For over 30 years, she has conducted research on computer-aided diagnosis, including computer vision, machine learning, and deep learning, in the areas of breast cancer, lung cancer, prostate cancer, brain injury, lupus, and bone diseases, and COVID-19.
Over her career, she has served on various NIH, DOD, and other funding agencies’ study sections, and is a former member of the NIBIB Advisory Council of NIH.
She is a former president of the American Association of Physicists in Medicine (AAPM) and a former president of the SPIE (the International Society of Optics and Photonics), and was the inaugural Editor-in-Chief of the SPIE Journal of Medical Imaging.
She is a member of the National Academy of Engineering (NAE) and was awarded the William D. Coolidge Gold Medal from the American Association of Physicists in Medicine, the highest award given by the AAPM. She is a Fellow of AAPM, AIMBE, SPIE, SBMR, IEEE, COS, and IAMBE, a recipient of the EMBS Academic Career Achievement Award, the SPIE Director's Award, the SPIE Harrison H. Barrett Award in Medical Imaging, the RSNA Honored Educator Award, and the RSNA Outstanding Researcher Award, and was a Hagler Institute Fellow at Texas A&M University. In 2013, Giger was named by the International Congress on Medical Physics (ICMP) as one of the 50 medical physicists with the most impact on the field in the last 50 years. In 2018, she received the iBIO iCON Innovator award.
She has more than 260 peer-reviewed publications (over 450 publications), has more than 30 patents and has mentored over 100 graduate students, residents, medical students, and undergraduate students.
Her research in computational image-based analyses of breast cancer for risk assessment, diagnosis, prognosis, and response to therapy has yielded various translated components, and she is now using these image-based phenotypes, i.e., “virtual biopsies” in imaging genomics association studies for discovery.
She extended her AI in medical imaging research to include the analysis of COVID-19 on CT and chest radiographs, and is contact PI on the NIH NIBIB-funded & ARPA-H-funded Medical Imaging and Data Resource Center (MIDRC; midrc.org).
She was a cofounder of Quantitative Insights, Inc., which started through the 2009-2010 New Venture Challenge at the University of Chicago. QI produced QuantX, which in 2017, became the first FDA-cleared, machine-learning-driven system to aid in cancer diagnosis (CADx). In 2019, QuantX was named one of TIME magazine's inventions of the year, and was bought by Qlarity Imaging.
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