2022 - Research.com Best Female Scientist Award
2016 - IEEE Fellow For contributions to computer-aided biomedical imaging and diagnosis
2014 - SPIE Fellow
2010 - Member of the National Academy of Engineering For contributions to digital signal analysis for improved cancer detection and treatment and for innovations in interdisciplinary training.
2000 - Fellow of the Indian National Academy of Engineering (INAE)
Artificial intelligence, Radiology, Computer-aided diagnosis, Mammography and Medical imaging are her primary areas of study. Her biological study spans a wide range of topics, including Digital mammography, Computer vision and Pattern recognition. The various areas that Maryellen L. Giger examines in her Radiology study include Malignancy and Lung.
Her Computer-aided diagnosis research incorporates themes from Lesion, Image segmentation, Magnetic resonance imaging, Medical physics and Prostate cancer. Her study on Mammography is covered under Breast cancer. Her Medical imaging research includes elements of Image processing, Digital radiography, Data mining and Nuclear medicine.
Maryellen L. Giger mainly investigates Artificial intelligence, Radiology, Computer-aided diagnosis, Breast cancer and Mammography. Maryellen L. Giger combines subjects such as Computer vision and Pattern recognition with her study of Artificial intelligence. Her Radiology research incorporates elements of Lesion and Malignancy.
Her work deals with themes such as Image processing, Breast ultrasound, Feature and Medical imaging, which intersect with Computer-aided diagnosis. Her Breast cancer study combines topics from a wide range of disciplines, such as Pathology, Magnetic resonance imaging and Receiver operating characteristic. Her work on Digital mammography and Breast imaging as part of general Mammography study is frequently linked to Workstation, bridging the gap between disciplines.
Maryellen L. Giger focuses on Artificial intelligence, Breast cancer, Receiver operating characteristic, Deep learning and Pattern recognition. Convolutional neural network, Segmentation, Medical imaging, Feature extraction and Support vector machine are subfields of Artificial intelligence in which her conducts study. Her Breast cancer research includes themes of Radiomics, Radiology, Feature and Confidence interval.
Her research investigates the connection between Radiology and topics such as Malignancy that intersect with issues in Breast imaging. Her Receiver operating characteristic research is multidisciplinary, incorporating perspectives in Lesion, Multiple comparisons problem, Computer-aided diagnosis, Magnetic resonance imaging and Feature selection. As a part of the same scientific family, Maryellen L. Giger mostly works in the field of Pattern recognition, focusing on Dynamic contrast-enhanced MRI and, on occasion, Recurrent neural network.
Artificial intelligence, Breast cancer, Receiver operating characteristic, Radiology and Medical imaging are her primary areas of study. Her study in Artificial intelligence is interdisciplinary in nature, drawing from both Cancer and Pattern recognition. Her Pattern recognition study combines topics in areas such as Multiparametric Magnetic Resonance Imaging, Mammography and Region of interest.
The concepts of her Breast cancer study are interwoven with issues in Magnetic resonance imaging, Radiomics and Oncology. Her Radiology research is multidisciplinary, incorporating perspectives in Lesion, Area under the curve, Cross-validation, Progression-free survival and Luminal a. Maryellen L. Giger interconnects Contextual image classification, Image segmentation and Medical physics in the investigation of issues within Medical imaging.
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Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer.
Yuzheng Wu;M. L. Giger;Kunio Doi;C. J. Vyborny.
Artificial intelligence in cancer imaging: Clinical challenges and applications.
Wenya Linda Bi;Ahmed Hosny;Matthew B. Schabath;Maryellen L. Giger.
CA: A Cancer Journal for Clinicians (2019)
Computerized Detection of Pulmonary Nodules on CT Scans
Samuel G. Armato;Maryellen L. Giger;Catherine J. Moran;James T. Blackburn.
Deep learning in medical imaging and radiation therapy.
Berkman Sahiner;Aria Pezeshk;Lubomir M. Hadjiiski;Xiaosong Wang.
Medical Physics (2019)
Improving breast cancer diagnosis with computer-aided diagnosis
Yulei Jiang;Robert M. Nishikawa;Robert A. Schmidt;Charles E. Metz.
Academic Radiology (1999)
A Fuzzy C-Means (FCM)-Based Approach for Computerized Segmentation of Breast Lesions in Dynamic Contrast-Enhanced MR Images1
Weijie Chen;Maryellen L. Giger;Ulrich Bick.
Academic Radiology (2006)
Digital mammographic tumor classification using transfer learning from deep convolutional neural networks
Benjamin Q. Huynh;Hui Li;Maryellen L. Giger.
Journal of medical imaging (2016)
MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays.
Hui Li;Yitan Zhu;Elizabeth S. Burnside;Karen Drukker.
Machine Learning in Medical Imaging.
Maryellen L. Giger.
Journal of The American College of Radiology (2018)
Image feature analysis and computer-aided diagnosis in digital radiography. 3. Automated detection of nodules in peripheral lung fields.
Maryellen Lissak Giger;Kunio Doi;Heber MacMahon.
Medical Physics (1988)
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