Maryellen L. Giger

Affiliations: 
Medical Physics University of Chicago, Chicago, IL 
Area:
Radiation Physics, Radiology
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Parents

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 Miguel Awschalom research assistant
Rose Agnes Carney research assistant
Robert Goodwin research assistant
Ralph D. Meeker research assistant Illinois Benedictine College
 Vernon Wynn research assistant University of Exeter
Kunio Doi grad student Chicago

Children

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Samuel G. Armato grad student Chicago
Natalie Baughan grad student Chicago
Lindsay Douglas grad student Chicago
Jordan Fuhrman grad student Chicago
Isabelle Hu grad student Chicago
Matthew A. Kupinski grad student 2000 Chicago
Michael R. Chinander grad student 2004 Chicago
Weijie Chen grad student 2007 Chicago
Joel R. Wilkie grad student 2007 Chicago
Neha Bhooshan grad student 2010 Chicago
Yading Yuan grad student 2010 Chicago
Martin M. Andrews grad student 2014 Chicago

Collaborators

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Heather M. Whitney collaborator 2017-
BETA: Related publications

Publications

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Hu Q, Whitney HM, Giger ML. (2020) Radiomics methodology for breast cancer diagnosis using multiparametric magnetic resonance imaging. Journal of Medical Imaging (Bellingham, Wash.). 7: 044502
Hu Q, Whitney HM, Giger ML. (2020) A deep learning methodology for improved breast cancer diagnosis using multiparametric MRI. Scientific Reports. 10: 10536
Whitney HM, Li H, Ji Y, et al. (2020) Harmonization of radiomic features of breast lesions across international DCE-MRI datasets. Journal of Medical Imaging (Bellingham, Wash.). 7: 012707
Crosby J, Rhines T, Li F, et al. (2020) Deep convolutional neural networks in the classification of dual-energy thoracic radiographic views for efficient workflow: analysis on over 6500 clinical radiographs. Journal of Medical Imaging (Bellingham, Wash.). 7: 016501
Drukker K, Edwards A, Doyle C, et al. (2019) Breast MRI radiomics for the pretreatment prediction of response to neoadjuvant chemotherapy in node-positive breast cancer patients. Journal of Medical Imaging (Bellingham, Wash.). 6: 034502
Ji Y, Li H, Edwards AV, et al. (2019) Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution. Cancer Imaging : the Official Publication of the International Cancer Imaging Society. 19: 64
Yeh AC, Li H, Zhu Y, et al. (2019) Radiogenomics of breast cancer using dynamic contrast enhanced MRI and gene expression profiling. Cancer Imaging : the Official Publication of the International Cancer Imaging Society. 19: 48
Miller TA, Robinson KR, Li H, et al. (2019) 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. Quantitative Imaging in Medicine and Surgery. 9: 399-408
Antropova N, Huynh B, Li H, et al. (2019) Breast lesion classification based on dynamic contrast-enhanced magnetic resonance images sequences with long short-term memory networks. Journal of Medical Imaging (Bellingham, Wash.). 6: 011002
Robinson K, Li H, Lan L, et al. (2019) Radiomics Robustness Assessment and Classification Evaluation: A Two-Stage Method Demonstrated on Multi-Vendor FFDM. Medical Physics
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