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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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Shenouda M, Flerlage I, Kaveti A, et al. (2023) Assessment of a deep learning model for COVID-19 classification on chest radiographs: a comparison across image acquisition techniques and clinical factors. Journal of Medical Imaging (Bellingham, Wash.). 10: 064504
Baughan N, Whitney HM, Drukker K, et al. (2023) Sequestration of imaging studies in MIDRC: stratified sampling to balance demographic characteristics of patients in a multi-institutional data commons. Journal of Medical Imaging (Bellingham, Wash.). 10: 064501
Douglas L, Bhattacharjee R, Fuhrman J, et al. (2023) U-Net breast lesion segmentations for breast dynamic contrast-enhanced magnetic resonance imaging. Journal of Medical Imaging (Bellingham, Wash.). 10: 064502
Li H, Drukker K, Hu Q, et al. (2023) Predicting intensive care need for COVID-19 patients using deep learning on chest radiography. Journal of Medical Imaging (Bellingham, Wash.). 10: 044504
Whitney HM, Drukker K, Vieceli M, et al. (2023) Role of sureness in evaluating AI/CADx: Lesion-based repeatability of machine learning classification performance on breast MRI. Medical Physics
Chen W, Sá RC, Bai Y, et al. (2023) Machine learning with multimodal data for COVID-19. Heliyon. 9: e17934
Whitney HM, Baughan N, Myers KJ, et al. (2023) Longitudinal assessment of demographic representativeness in the Medical Imaging and Data Resource Center open data commons. Journal of Medical Imaging (Bellingham, Wash.). 10: 61105
Baughan N, Li H, Lan L, et al. (2023) Radiomic and deep learning characterization of breast parenchyma on full field digital mammograms and specimen radiographs: a pilot study of a potential cancer field effect. Journal of Medical Imaging (Bellingham, Wash.). 10: 044501
Drukker K, Chen W, Gichoya J, et al. (2023) Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment. Journal of Medical Imaging (Bellingham, Wash.). 10: 061104
Li H, Robinson K, Lan L, et al. (2023) Temporal Machine Learning Analysis of Prior Mammograms for Breast Cancer Risk Prediction. Cancers. 15
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