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Medicine

D-Index
105
Citations
35547
World Ranking
6831
National Ranking
3606

Research.com Recognitions

  • 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)

Overview

Maryellen L. Giger is affiliated with the University of Chicago in the United States. Their research spans primarily the field of Medicine with a particular focus on Radiology, Nuclear Medicine and Imaging. Other key subfields include Artificial Intelligence, Health Informatics, Pulmonary and Respiratory Medicine, and Oncology.

The main research topics addressed by Maryellen L. Giger include:

  • Radiomics and Machine Learning in Medical Imaging
  • AI in cancer detection
  • Artificial Intelligence in Healthcare and Education
  • COVID-19 diagnosis using AI
  • MRI in cancer diagnosis
  • Medical Imaging Techniques and Applications
  • Lung Cancer Diagnosis and Treatment

The publication record includes contributions to frequent venues such as:

  • Journal of Medical Imaging
  • Medical Physics
  • Medical Imaging 2020: Computer-Aided Diagnosis
  • arXiv (Cornell University)
  • Radiology Artificial Intelligence

Notable recent papers authored or co-authored by Maryellen L. Giger are:

  • "Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update" (2024), Radiology Artificial Intelligence
  • "Criteria for the translation of radiomics into clinically useful tests" (2022), Nature Reviews Clinical Oncology
  • "A deep learning methodology for improved breast cancer diagnosis using multiparametric MRI" (2020), Scientific Reports
  • "A review of explainable and interpretable AI with applications in COVID-19 imaging" (2021), Medical Physics
  • "Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment" (2023), Journal of Medical Imaging

Frequent co-authors in their research include:

  • Karen Drukker
  • Heather M. Whitney
  • Hui Li
  • Jordan Fuhrman
  • Marcus R. Clark

Maryellen L. Giger has been recognized with several awards, including:

  • IEEE Fellow (2016) for contributions to computer-aided biomedical imaging and diagnosis
  • SPIE Fellow (2014)
  • Member of the National Academy of Engineering (2010) for work in digital signal analysis for improved cancer detection and treatment and innovations in interdisciplinary training
  • Fellow of the Indian National Academy of Engineering (INAE) (2000)

Best Publications

  • Artificial intelligence in cancer imaging: Clinical challenges and applications.

    Wenya Linda Bi;Ahmed Hosny;Matthew B. Schabath;Maryellen L. Giger

  • Deep learning in medical imaging and radiation therapy.

    Berkman Sahiner;Aria Pezeshk;Lubomir M. Hadjiiski;Xiaosong Wang

  • 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

  • Machine Learning in Medical Imaging.

    Maryellen L. Giger

  • Computerized Detection of Pulmonary Nodules on CT Scans

    Samuel G. Armato;Maryellen L. Giger;Catherine J. Moran;James T. Blackburn

  • Digital mammographic tumor classification using transfer learning from deep convolutional neural networks

    Benjamin Q. Huynh;Hui Li;Maryellen L. Giger

  • 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

  • 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

  • Improving breast cancer diagnosis with computer-aided diagnosis

    Yulei Jiang;Robert M. Nishikawa;Robert A. Schmidt;Charles E. Metz

  • Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM.

    Maryellen L. Giger;Heang Ping Chan;John M Boone

  • 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

  • Automated seeded lesion segmentation on digital mammograms

    M.A. Kupinski;M.L. Giger

  • Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images.

    Weijie Chen;Maryellen L. Giger;Hui Li;Ulrich Bick

  • A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets.

    Natalia Antropova;Benjamin Q. Huynh;Maryellen L. Giger

  • Malignant and benign clustered microcalcifications: automated feature analysis and classification.

    Y Jiang;R M Nishikawa;D E Wolverton;C E Metz

  • Automated detection of lung nodules in CT scans: Preliminary results

    Samuel G. Armato;Maryellen L. Giger;Heber MacMahon

  • Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program

    Samuel G. Armato;Feng Li;Maryellen L. Giger;Heber MacMahon

  • Computerized Detection of Pulmonary Nodules in Computed Tomography Images

    Maryellen L. Giger;Kyongtae T. Bae;Heber MacMAHON

  • Computerized detection of masses in digital mammograms: analysis of bilateral subtraction images.

    Fang-Fang Yin;Maryellen L. Giger;Kunio Doi;Charles E. Metz

  • Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set.

    Hui Li;Yitan Zhu;Elizabeth S Burnside;Erich Huang

Frequent Co-Authors

Kunio Doi
Kunio Doi University of Chicago
Heber MacMahon
Heber MacMahon University of Chicago
Robert M. Nishikawa
Robert M. Nishikawa University of Pittsburgh
Charles E. Metz
Charles E. Metz University of Chicago
Olufunmilayo I. Olopade
Olufunmilayo I. Olopade University of Chicago
Marcus R. Clark
Marcus R. Clark University of Chicago
Xiaochuan Pan
Xiaochuan Pan University of Chicago
Kenji Suzuki
Kenji Suzuki Tokyo Institute of Technology
Heang Ping Chan
Heang Ping Chan University of Michigan–Ann Arbor
Hiroshi Fujita
Hiroshi Fujita Gifu University

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