Maryellen L. Giger

26.2k total citations · 8 hit papers
477 papers, 18.6k citations indexed

About

Maryellen L. Giger is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Maryellen L. Giger has authored 477 papers receiving a total of 18.6k indexed citations (citations by other indexed papers that have themselves been cited), including 333 papers in Radiology, Nuclear Medicine and Imaging, 239 papers in Artificial Intelligence and 142 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Maryellen L. Giger's work include Radiomics and Machine Learning in Medical Imaging (244 papers), AI in cancer detection (229 papers) and MRI in cancer diagnosis (86 papers). Maryellen L. Giger is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (244 papers), AI in cancer detection (229 papers) and MRI in cancer diagnosis (86 papers). Maryellen L. Giger collaborates with scholars based in United States, China and Germany. Maryellen L. Giger's co-authors include Kunio Doi, Hui Li, Carl J. Vyborny, Karen Drukker, Heber MacMahon, Charles E. Metz, Ulrich Bick, Robert M. Nishikawa, Benjamin Q. Huynh and Robert A. Schmidt and has published in prestigious journals such as Journal of Clinical Investigation, SHILAP Revista de lepidopterología and Nature Immunology.

In The Last Decade

Maryellen L. Giger

460 papers receiving 17.9k citations

Hit Papers

Artificial intelligence in cancer imaging: Clinical chall... 2016 2026 2019 2022 2019 2018 2018 2016 2016 250 500 750 1000
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Artificial intelligence in cancer imaging: Clinical challenges and applications
    2019 1.2k citations Maryellen L. Giger et al. profile →
  2. Deep learning in medical imaging and radiation therapy
    2018 527 citations Berkman Sahiner, Karen Drukker et al. Medical Physics profile →
  3. Machine Learning in Medical Imaging
    2018 405 citations Maryellen L. Giger profile →
  4. Digital mammographic tumor classification using transfer learning from deep convolutional neural networks
    2016 393 citations Hui Li, Maryellen L. Giger et al. Journal of Medical Imaging profile →
  5. 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
    2016 373 citations Hui Li, Elizabeth S. Burnside et al. Radiology profile →
  6. A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets
    2017 298 citations Maryellen L. Giger et al. Medical Physics profile →
  7. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set
    2016 287 citations Hui Li, Elizabeth S. Burnside et al. profile →
  8. Criteria for the translation of radiomics into clinically useful tests
    2022 127 citations Erich P. Huang, Maryellen L. Giger et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Maryellen L. Giger United States 73 12.5k 9.3k 5.2k 3.3k 2.3k 477 18.6k
Heang‐Ping Chan United States 61 8.0k 0.6× 7.0k 0.8× 4.6k 0.9× 2.8k 0.9× 2.1k 0.9× 410 12.8k
Jeroen van der Laak Netherlands 48 7.2k 0.6× 7.8k 0.8× 1.9k 0.4× 4.1k 1.2× 2.0k 0.9× 207 17.0k
Anant Madabhushi United States 75 12.0k 1.0× 11.2k 1.2× 4.1k 0.8× 6.1k 1.9× 2.7k 1.2× 544 23.3k
Daniel L. Rubin United States 63 8.2k 0.7× 5.9k 0.6× 2.5k 0.5× 2.2k 0.7× 1.7k 0.8× 366 15.9k
Geert Litjens Netherlands 37 8.4k 0.7× 7.9k 0.9× 3.1k 0.6× 4.4k 1.3× 2.1k 0.9× 101 16.1k
Ronald M. Summers United States 62 9.3k 0.7× 5.2k 0.6× 3.5k 0.7× 4.3k 1.3× 3.5k 1.5× 501 18.9k
Bram van Ginneken Netherlands 80 21.2k 1.7× 9.2k 1.0× 8.4k 1.6× 10.4k 3.1× 4.7k 2.0× 432 34.4k
Francesco Ciompi Netherlands 28 6.6k 0.5× 5.7k 0.6× 2.1k 0.4× 3.4k 1.0× 1.8k 0.8× 103 12.6k
Nico Karssemeijer Netherlands 53 4.9k 0.4× 5.6k 0.6× 2.7k 0.5× 1.9k 0.6× 605 0.3× 200 8.6k
Jianhua Yao United States 50 4.5k 0.4× 3.3k 0.4× 1.5k 0.3× 2.6k 0.8× 2.1k 0.9× 377 12.7k

Countries citing papers authored by Maryellen L. Giger

Since Specialization
Citations

This map shows the geographic impact of Maryellen L. Giger's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Maryellen L. Giger with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maryellen L. Giger more than expected).

Fields of papers citing papers by Maryellen L. Giger

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Maryellen L. Giger. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Maryellen L. Giger. The network helps show where Maryellen L. Giger may publish in the future.

Co-authorship network of co-authors of Maryellen L. Giger

This figure shows the co-authorship network connecting the top 25 collaborators of Maryellen L. Giger. A scholar is included among the top collaborators of Maryellen L. Giger based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Maryellen L. Giger. Maryellen L. Giger is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Li, Hui, Lan Li, Brendan M. Finnerty, et al.. (2025). Multi-institutional development and testing of attention-enhanced deep learning segmentation of thyroid nodules on ultrasound. International Journal of Computer Assisted Radiology and Surgery. 20(2). 259–267. 2 indexed citations
2.
Klarqvist, Marcus D. R., Miao Li, Kibaek Kim, et al.. (2024). Enabling end-to-end secure federated learning in biomedical research on heterogeneous computing environments with APPFLx. Computational and Structural Biotechnology Journal. 28. 29–39. 4 indexed citations
3.
Drukker, Karen, et al.. (2024). Radiomics and quantitative multi-parametric MRI for predicting uterine fibroid growth. 109–109. 1 indexed citations
4.
Drukker, Karen, Berkman Sahiner, Grace Hyun J. Kim, et al.. (2024). MIDRC-MetricTree: a decision tree-based tool for recommending performance metrics in artificial intelligence-assisted medical image analysis. Journal of Medical Imaging. 11(2). 24504–24504. 1 indexed citations
5.
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Khosravi, Bardia, Elham Mahmoudi, Pouria Rouzrokh, et al.. (2024). A Guideline for Open-Source Tools to Make Medical Imaging Data Ready for Artificial Intelligence Applications: A Society of Imaging Informatics in Medicine (SIIM) Survey. Journal of Imaging Informatics in Medicine. 37(5). 2015–2024.
7.
Drukker, Karen, et al.. (2023). U-Net breast lesion segmentations for breast dynamic contrast-enhanced magnetic resonance imaging. Journal of Medical Imaging. 10(6). 64502–64502. 1 indexed citations
8.
Li, Hui, et al.. (2023). User experience evaluation for MIDRC AI interface. 2–2. 1 indexed citations
9.
Kalpathy-Cramer, Jayashree, et al.. (2023). Assessing robustness of a deep-learning model for COVID-19 classification on chest radiographs. 8. 13–13. 1 indexed citations
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Li, Hui, Lan Li, C. W. Chan, et al.. (2023). Temporal Machine Learning Analysis of Prior Mammograms for Breast Cancer Risk Prediction. Cancers. 15(7). 2141–2141. 9 indexed citations
13.
Giger, Maryellen L.. (2021). Medical Imaging and Data Resource Center: Imaging in Covid. Medical Physics. 48(6). 1–1. 2 indexed citations
14.
Li, Feng, Samuel G. Armato, Roger Engelmann, et al.. (2021). Anatomic Point–Based Lung Region with Zone Identification for Radiologist Annotation and Machine Learning for Chest Radiographs. Journal of Digital Imaging. 34(4). 922–931. 1 indexed citations
15.
Liarski, Vladimir M., Nicholas van Panhuys, Junting Ai, et al.. (2019). Quantifying in situ adaptive immune cell cognate interactions in humans. Nature Immunology. 20(4). 503–513. 24 indexed citations
16.
Sutton, Elizabeth J., Erich P. Huang, Karen Drukker, et al.. (2017). Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes. European Radiology Experimental. 1(1). 22–22. 27 indexed citations
17.
Drukker, Karen, et al.. (2008). Breast US Computer-aided Diagnosis Workstation: Performance with a Large Clinical Diagnostic Population. Radiology. 248(2). 392–397. 43 indexed citations
18.
Huo, Zhimin, et al.. (2000). Computerized analysis of mammographic parenchymal patterns for breast cancer risk assessment: Feature selection. Medical Physics. 27(1). 4–12. 70 indexed citations
19.
Kupinski, Matthew A. & Maryellen L. Giger. (1997). Investigation of regularized neural networks for the computerized detection of mass lesions in digital mammograms. 3. 1336–1339. 3 indexed citations
20.
Doi, Kunio, Maryellen L. Giger, Robert M. Nishikawa, et al.. (1995). Potential usefulness of digital imaging in clinical diagnostic radiology: Computer-aided diagnosis. Journal of Digital Imaging. 8(S1). 2–7. 10 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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