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Giger, Maryellen L.

One or more keywords matched the following properties of Giger, Maryellen L.

Property	Value
keywords	computer-aided diagnosis, machine learning, breast cancer, deep learning, radiomics, COVID-19
overview	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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keywords

computer-aided diagnosis, machine learning, breast cancer, deep learning, radiomics, COVID-19

overview

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.

One or more keywords matched the following items that are connected to Giger, Maryellen L.

Item Type	Name
Academic Article	Digital mammographic tumor classification using transfer learning from deep convolutional neural networks.
Academic Article	Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms.
Academic Article	Machine Learning in Medical Imaging.
Academic Article	Transfer Learning From Convolutional Neural Networks for Computer-Aided Diagnosis: A Comparison of Digital Breast Tomosynthesis and Full-Field Digital Mammography.
Academic Article	Special Section Guest Editorial: Radiomics and Deep Learning.
Academic Article	Deep learning in medical imaging and radiation therapy.
Academic Article	Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution.
Academic Article	A deep learning methodology for improved breast cancer diagnosis using multiparametric MRI.
Academic Article	Cascaded deep transfer learning on thoracic CT in COVID-19 patients treated with steroids.
Academic Article	Automated mesenchymal stem cell segmentation and machine learning-based phenotype classification using morphometric and textural analysis.
Academic Article	Comparison of Breast MRI Tumor Classification Using Human-Engineered Radiomics, Transfer Learning From Deep Convolutional Neural Networks, and Fusion Methods.
Academic Article	Artificial Intelligence and Cellular Segmentation in Tissue Microscopy Images.
Academic Article	Anatomic Point-Based Lung Region with Zone Identification for Radiologist Annotation and Machine Learning for Chest Radiographs.
Academic Article	Role of standard and soft tissue chest radiography images in deep-learning-based early diagnosis of COVID-19.
Academic Article	Machine Learning for Early Detection of Hypoxic-Ischemic Brain Injury After Cardiac Arrest.
Academic Article	Comment on "Machine Learning for Early Detection of Hypoxic-Ischemic Brain Injury After Cardiac Arrest" Submitted by Noah Salomon Molinski et al.
Academic Article	Impact of continuous learning on diagnostic breast MRI AI: evaluation on an independent clinical dataset.
Academic Article	A machine-learning algorithm for distinguishing malignant from benign indeterminate thyroid nodules using ultrasound radiomic features.
Academic Article	Evaluation of emphysema on thoracic low-dose CTs through attention-based multiple instance deep learning.
Academic Article	MIDRC CRP10 AI interface-an integrated tool for exploring, testing and visualization of AI models.
Concept	Machine Learning
Academic Article	Temporal Machine Learning Analysis of Prior Mammograms for Breast Cancer Risk Prediction.
Academic Article	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.
Academic Article	Machine learning with multimodal data for COVID-19.
Academic Article	Predicting intensive care need for COVID-19 patients using deep learning on chest radiography.
Academic Article	Role of sureness in evaluating AI/CADx: Lesion-based repeatability of machine learning classification performance on breast MRI.
Academic Article	Pilot study of machine learning in the task of distinguishing high and low-grade pediatric hydronephrosis on ultrasound.
Academic Article	Assessment of a deep learning model for COVID-19 classification on chest radiographs: a comparison across image acquisition techniques and clinical factors.
Academic Article	Past, Present, and Future of Machine Learning and Artificial Intelligence for Breast Cancer Screening.

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