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Bram van Ginneken

Bram van Ginneken

Award Badge
Computer Science
Netherlands
2026

D-Index & Metrics

Computer Science

D-Index
108
Citations
70671
World Ranking
253
National Ranking
2

Medicine

D-Index
116
Citations
73622
World Ranking
4268
National Ranking
163

Research.com Recognitions

  • 2026 - Research.com Computer Science in Netherlands Leader Award
  • 2025 - Research.com Computer Science in Netherlands Leader Award
  • 2023 - Research.com Computer Science in Netherlands Leader Award
  • 2022 - Research.com Computer Science in Netherlands Leader Award

Overview

Bram van Ginneken is affiliated with Radboud University in the Netherlands. Their research primarily focuses on the field of Medicine with a notable concentration in Radiology, Nuclear Medicine and Imaging. Additional subfields of study include Pulmonary and Respiratory Medicine, Artificial Intelligence, Biomedical Engineering, and Health Informatics.

The scientist's work extensively covers topics related to Radiomics and Machine Learning in Medical Imaging, COVID-19 diagnosis using AI, and Lung Cancer Diagnosis and Treatment. Other main topics include Artificial Intelligence in Healthcare and Education, AI in cancer detection, Medical Imaging and Analysis, and Chronic Obstructive Pulmonary Disease (COPD) Research.

Notable recent publications include:

  • The Medical Segmentation Decathlon (2022), published in Nature Communications
  • CO-RADS: A Categorical CT Assessment Scheme for Patients Suspected of Having COVID-19-Definition and Evaluation (2020), published in Radiology
  • Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study (2020), published in The Lancet Oncology
  • Deep learning for chest X-ray analysis: A survey (2021), published in Medical Image Analysis
  • Artificial intelligence in radiology: 100 commercially available products and their scientific evidence (2021), published in European Radiology

Bram van Ginneken frequently collaborates with several researchers, including:

  • Keelin Murphy
  • Colin Jacobs
  • Mathias Prokop
  • Matthieu Rutten
  • Alessa Hering

Publication venues where Bram van Ginneken has consistently contributed include Zenodo (CERN European Organization for Nuclear Research), arXiv (Cornell University), Medical Image Analysis, European Radiology, and Radiology.

Best Publications

  • A survey on deep learning in medical image analysis

    Geert J. S. Litjens;Thijs Kooi;Babak Ehteshami Bejnordi;Arnaud Arindra Adiyoso Setio

  • Ridge-based vessel segmentation in color images of the retina

    J. Staal;M.D. Abramoff;M. Niemeijer;M.A. Viergever

  • Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.

    Babak Ehteshami Bejnordi;Mitko Veta;Paul Johannes van Diest;Bram van Ginneken

  • Reflectance and texture of real-world surfaces

    Kristin J. Dana;Bram van Ginneken;Shree K. Nayar;Jan J. Koenderink

  • Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique

    Hayit Greenspan;Bram van Ginneken;Ronald M. Summers

  • A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises

    S. Kevin Zhou;Hayit Greenspan;Christos Davatzikos;James S. Duncan

  • Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks

    Arnaud Arindra Adiyoso Setio;Francesco Ciompi;Geert Litjens;Paul Gerke

  • Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets

    T. Heimann;B. van Ginneken;M.A. Styner;Y. Arzhaeva

  • Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge.

    Arnaud Arindra Adiyoso Setio;Alberto Traverso;Thomas de Bel;Moira S.N. Berens

  • Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis

    Geert Litjens;Clara I. Sánchez;Nadya Timofeeva;Meyke Hermsen

  • The Liver Tumor Segmentation Benchmark (LiTS)

    Patrick Bilic;Patrick Ferdinand Christ;Eugene Vorontsov;Grzegorz Chlebus

  • The Medical Segmentation Decathlon

    Michela Antonelli;Annika Reinke;Spyridon Bakas;Keyvan Farahani

  • Comparative study of retinal vessel segmentation methods on a new publicly available database

    Meindert Niemeijer;Meindert Niemeijer;Joes Staal;Bram van Ginneken;Marco Loog

  • Large scale deep learning for computer aided detection of mammographic lesions

    Thijs Kooi;Geert J. S. Litjens;Bram van Ginneken;Albert Gubern-Mérida

  • CO-RADS: A Categorical CT Assessment Scheme for Patients Suspected of Having COVID-19-Definition and Evaluation.

    Mathias Prokop;Wouter van Everdingen;Tjalco van Rees Vellinga;Henriëtte Quarles van Ufford

  • A large annotated medical image dataset for the development and evaluation of segmentation algorithms

    Amber L. Simpson;Michela Antonelli;Spyridon Bakas;Michel Bilello

  • Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge.

    Geert J. S. Litjens;Robert Toth;Wendy J. M. van de Ven;Caroline Hoeks

  • Computer analysis of computed tomography scans of the lung: a survey

    I. Sluimer;A. Schilham;M. Prokop;B. van Ginneken

  • Active shape model segmentation with optimal features

    B. van Ginneken;A.F. Frangi;J.J. Staal;B.M. ter Haar Romeny

  • Computer-aided diagnosis in chest radiography: a survey

    B. Van Ginneken;B.M. Ter Haar Romeny;M.A. Viergever

  • Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database.

    Bram van Ginneken;Mikkel Bille Stegmann;Marco Loog

Frequent Co-Authors

Mathias Prokop
Mathias Prokop Radboud University
Clara I. Sánchez
Clara I. Sánchez University of Amsterdam
Max A. Viergever
Max A. Viergever Utrecht University
Meindert Niemeijer
Meindert Niemeijer Digital Diagnostics Inc.
Michael D. Abràmoff
Michael D. Abràmoff University of Iowa
Matthijs Oudkerk
Matthijs Oudkerk University of Groningen
Harry J. de Koning
Harry J. de Koning Erasmus University Rotterdam
Ivana Išgum
Ivana Išgum University of Amsterdam
Willem P.Th.M. Mali
Willem P.Th.M. Mali Utrecht University
Carel B. Hoyng
Carel B. Hoyng Radboud University

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