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Bradley J. Erickson

Bradley J. Erickson

D-Index & Metrics

Computer Science

D-Index
51
Citations
12501
World Ranking
5275
National Ranking
2430

Overview

Bradley J. Erickson is affiliated with the Mayo Clinic in the United States. Their research primarily focuses on medicine, with a particular concentration in radiology, nuclear medicine, and imaging. They have contributed extensively to subfields such as artificial intelligence, surgery, biomedical engineering, and neurology.

The scientist's main topics of work include:

  • Radiomics and Machine Learning in Medical Imaging
  • Artificial Intelligence in Healthcare and Education
  • Advanced X-ray and CT Imaging
  • AI in cancer detection
  • Glioma Diagnosis and Treatment
  • COVID-19 diagnosis using AI
  • Orthopaedic implants and arthroplasty

Among their recent publications are:

  • "Association of Maximal Extent of Resection of Contrast-Enhanced and Non-Contrast-Enhanced Tumor With Survival Within Molecular Subgroups of Patients With Newly Diagnosed Glioblastoma," 2020, JAMA Oncology
  • "Magician's Corner: 9. Performance Metrics for Machine Learning Models," 2021, Radiology Artificial Intelligence
  • "Consensus recommendations for a dynamic susceptibility contrast MRI protocol for use in high-grade gliomas," 2020, Neuro-Oncology
  • "The RSNA International COVID-19 Open Radiology Database (RICORD)," 2021, Radiology
  • "Generative Adversarial Networks to Synthesize Missing T1 and FLAIR MRI Sequences for Use in a Multisequence Brain Tumor Segmentation Model," 2021, Radiology

Bradley J. Erickson collaborates frequently with coauthors including Shahriar Faghani, Bardia Khosravi, Pouria Rouzrokh, Gian Marco Conte, and Mana Moassefi.

Their work appears most often in the following publication venues:

  • Radiology Artificial Intelligence
  • Neuro-Oncology
  • Radiology
  • The Journal of Arthroplasty
  • Journal of Imaging Informatics in Medicine

Best Publications

  • Machine Learning for Medical Imaging.

    Bradley J. Erickson;Panagiotis Korfiatis;Zeynettin Akkus;Timothy L. Kline

  • Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions

    Zeynettin Akkus;Alfiia Galimzianova;Assaf Hoogi;Daniel L. Rubin

  • Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials

    Benjamin M. Ellingson;Martin Bendszus;Martin Bendszus;Jerrold Boxerman;Daniel Barboriak

  • The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload.

    Robert J. McDonald;Kara M. Schwartz;Laurence J. Eckel;Felix E. Diehn

  • The RSNA Pediatric Bone Age Machine Learning Challenge.

    Safwan S. Halabi;Luciano M. Prevedello;Jayashree Kalpathy-Cramer;Artem B. Mamonov

  • Initial clinical experience in MR imaging of the brain with a fast fluid- attenuated inversion-recovery pulse sequence

    J N Rydberg;C A Hammond;R C Grimm;B J Erickson

  • A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop.

    Curtis P. Langlotz;Bibb Allen;Bradley J. Erickson;Jayashree Kalpathy-Cramer

  • Automated Abdominal Segmentation of CT Scans for Body Composition Analysis Using Deep Learning

    Alexander D. Weston;Panagiotis Korfiatis;Timothy L. Kline;Kenneth A. Philbrick

  • A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence-Powered Ultrasound for Improving Clinical Workflow

    Zeynettin Akkus;Jason Cai;Arunnit Boonrod;Atefeh Zeinoddini

  • Magician’s Corner: 9. Performance Metrics for Machine Learning Models

    Bradley J. Erickson;Felipe Kitamura

  • Predicting Deletion of Chromosomal Arms 1p/19q in Low-Grade Gliomas from MR Images Using Machine Intelligence

    Zeynettin Akkus;Issa Ali;Jiří Sedlář;Jay P. Agrawal

  • Temporal Annotation in the Clinical Domain

    William F. Styler;Steven Bethard;Sean Finan;Martha Palmer

  • FLAIR Histogram Segmentation for Measurement of Leukoaraiosis Volume

    Clifford R. Jack;Peter C. O'Brien;Daniel W. Rettman;Maria M. Shiung

  • Residual Deep Convolutional Neural Network Predicts MGMT Methylation Status.

    Panagiotis Korfiatis;Timothy L. Kline;Daniel H. Lachance;Ian F. Parney

  • Toolkits and Libraries for Deep Learning.

    Bradley J. Erickson;Panagiotis Korfiatis;Zeynettin Akkus;Timothy Kline

  • Consensus recommendations for a dynamic susceptibility contrast MRI protocol for use in high-grade gliomas.

    Jerrold L. Boxerman;Chad C. Quarles;Leland S. Hu;Bradley J. Erickson

  • Irreversible Compression of Medical Images

    Bradley James Erickson

  • MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas

    Panagiotis Korfiatis;Timothy L. Kline;Lucie Coufalova;Daniel H. Lachance

  • Validation of neuroradiologic response assessment in gliomas: Measurement by RECIST, two-dimensional, computer-assisted tumor area, and computer-assisted tumor volume methods

    Evanthia Galanis;Jan C Buckner;Matthew J Maurer;Rene Sykora

  • Wavelet compression of medical images.

    Bradley J. Erickson;Armando Manduca;Patrice Palisson;Kenneth R. Persons

  • Performance of an Artificial Multi-observer Deep Neural Network for Fully Automated Segmentation of Polycystic Kidneys.

    Timothy L. Kline;Panagiotis Korfiatis;Marie E. Edwards;Jaime D. Blais

  • Determination of 10–20 system electrode locations using magnetic resonance image scanning with markers

    Terrence D. Lagerlund;Frank W. Sharbrough;Clifford R. Jack;Bradley J. Erickson

  • Deep Learning in Radiology: Does One Size Fit All?

    Bradley J. Erickson;Panagiotis Korfiatis;Timothy L. Kline;Zeynettin Akkus

Frequent Co-Authors

Jayashree Kalpathy-Cramer
Jayashree Kalpathy-Cramer Harvard University
Armando Manduca
Armando Manduca Mayo Clinic
Eliot L. Siegel
Eliot L. Siegel University of Maryland, Baltimore
Clifford R. Jack
Clifford R. Jack Mayo Clinic
Robert A. Greenes
Robert A. Greenes Arizona State University
Richard A. Robb
Richard A. Robb Mayo Clinic
Hans Lassmann
Hans Lassmann Medical University of Vienna
Daniel S. Marcus
Daniel S. Marcus Washington University in St. Louis
Susan M. Chang
Susan M. Chang University of California, San Francisco

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