Maryellen L. Giger, Ph.D. is the A.N. Pritzker 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, lupus, and bone diseases.
Over her career, she has served on various NIH, DOD, and other funding agencies’ study sections, and is now a member of the NIBIB Advisory Council of NIH.
She is a former president of the American Association of Physicists in Medicine 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, and IEEE, a recipient of the EMBS Academic Career Achievement Award, and is a current 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 200 peer-reviewed publications (over 300 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., these “virtual biopsies” in imaging genomics association studies for discovery.
She is a cofounder, equity holder, and scientific advisor of Quantitative Insights, Inc., which started through the 2009-2010 New Venture Challenge at the University of Chicago. QI produces QuantX, the first FDA-cleared, machine-learning-driven system to aid in cancer diagnosis (CADx).
University of Chicago
Chicago, IL
PhD - medical physics
1985
University of Exeter
Exeter, England
MSc - physics
1979
Illinois Benedictine College
Lisle, IL
BS - physics, math, health science
1978
Breast MRI radiomics for the pretreatment prediction of response to neoadjuvant chemotherapy in node-positive breast cancer patients.
Drukker K, Edwards A, Doyle C, Papaioannou J, Kulkarni K, Giger ML. Breast MRI radiomics for the pretreatment prediction of response to neoadjuvant chemotherapy in node-positive breast cancer patients. J Med Imaging (Bellingham). 2019 Jul; 6(3):034502.
PMID: 31592438
Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution.
Ji Y, Li H, Edwards AV, Papaioannou J, Ma W, Liu P, Giger ML. Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution. Cancer Imaging. 2019 Sep 18; 19(1):64.
PMID: 31533838
Artificial intelligence in the interpretation of breast cancer on MRI.
Sheth D, Giger ML. Artificial intelligence in the interpretation of breast cancer on MRI. J Magn Reson Imaging. 2019 Jul 25.
PMID: 31343790
Radiogenomics of breast cancer using dynamic contrast enhanced MRI and gene expression profiling.
Yeh AC, Li H, Zhu Y, Zhang J, Khramtsova G, Drukker K, Edwards A, McGregor S, Yoshimatsu T, Zheng Y, Niu Q, Abe H, Mueller J, Conzen S, Ji Y, Giger ML, Olopade OI. Radiogenomics of breast cancer using dynamic contrast enhanced MRI and gene expression profiling. Cancer Imaging. 2019 Jul 15; 19(1):48.
PMID: 31307537
Radiomics robustness assessment and classification evaluation: A two-stage method demonstrated on multivendor FFDM.
Robinson K, Li H, Lan L, Schacht D, Giger M. Radiomics robustness assessment and classification evaluation: A two-stage method demonstrated on multivendor FFDM. Med Phys. 2019 May; 46(5):2145-2156.
PMID: 30802972
Prognostic value of pre-treatment CT texture analysis in combination with change in size of the primary tumor in response to induction chemotherapy for HPV-positive oropharyngeal squamous cell carcinoma.
Miller TA, Robinson KR, Li H, Seiwert TY, Haraf DJ, Lan L, Giger ML, Ginat DT. Prognostic value of pre-treatment CT texture analysis in combination with change in size of the primary tumor in response to induction chemotherapy for HPV-positive oropharyngeal squamous cell carcinoma. Quant Imaging Med Surg. 2019 Mar; 9(3):399-408.
PMID: 31032187
Quantifying in situ adaptive immune cell cognate interactions in humans.
Liarski VM, Sibley A, van Panhuys N, Ai J, Chang A, Kennedy D, Merolle M, Germain RN, Giger ML, Clark MR. Quantifying in situ adaptive immune cell cognate interactions in humans. Nat Immunol. 2019 04; 20(4):503-513.
PMID: 30778242
Digital Mammography in Breast Cancer: Additive Value of Radiomics of Breast Parenchyma.
Li H, Mendel KR, Lan L, Sheth D, Giger ML. Digital Mammography in Breast Cancer: Additive Value of Radiomics of Breast Parenchyma. Radiology. 2019 04; 291(1):15-20.
PMID: 30747591
Artificial intelligence in cancer imaging: Clinical challenges and applications.
Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, Allison T, Arnaout O, Abbosh C, Dunn IF, Mak RH, Tamimi RM, Tempany CM, Swanton C, Hoffmann U, Schwartz LH, Gillies RJ, Huang RY, Aerts HJWL. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin. 2019 Mar; 69(2):127-157.
PMID: 30720861
Combined Benefit of Quantitative Three-Compartment Breast Image Analysis and Mammography Radiomics in the Classification of Breast Masses in a Clinical Data Set.
Drukker K, Giger ML, Joe BN, Kerlikowske K, Greenwood H, Drukteinis JS, Niell B, Fan B, Malkov S, Avila J, Kazemi L, Shepherd J. Combined Benefit of Quantitative Three-Compartment Breast Image Analysis and Mammography Radiomics in the Classification of Breast Masses in a Clinical Data Set. Radiology. 2019 03; 290(3):621-628.
PMID: 30526359
Fellow
Society of Breast MRI (SBMR)
2018
Crain's Chicago Notable Women in Education
Crain's Chicago
2018
iCON Innovator Award
IBIO Institute
2018
Hagler Institute Fellow
Texas A & M University
2017 - 2020
Fellow
IEEE
2016
Academic Career Achievement Award
Engineering in Medicine and Biology Society (EMBS)
2016
Visionary Award
Benedictine University
2015
William D. Coolidge Gold Medal
American Association of Physicists in Medicine (AAPM)
2015
Distinguished Investigator
Academy of Radiology Research
2015
Fellow
SPIE
2014
Distinguished Science Alumni Award
Benedictine University
2014
Named by the International Congress on Medical Physics (ICMP) as one of the 50 medical physicists wi
2013
Elected National Academy of Engineering
National Academy of Engineering
2010
Excellence Award
University of Chicago Paul Hodges Alumni Society
2009
Distinguished Alumni Award
Benedictine University
2006
Senior Member
IEEE
2005
Third Vice President (Honorary)
RSNA
2005
Fellow
AAPM
2001
Fellow
AIMBE
2000
Stauffer Award, Academic Radiology
1995
Sylvia Sorkin Greenfield Award
AAPM
1995
First Place Award, Young Investigators' Symposium
AAPM
1985
B.S. summa cum laude
Illinois Benedictine College
1978
Procopian Award
Illinois Benedictine College
1978
Rotary International Fellowship
1978 - 1979
Rev. Shonka, O.S.B. Scholarship Award in Physics
Illinois Benedictine College
1977
President's Scholarship Award
Illinois Benedictine College
1975 - 1977