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Nico Karssemeijer

Nico Karssemeijer

Award Badge
Computer Science
Netherlands
2025

D-Index & Metrics

Computer Science

D-Index
74
Citations
19950
World Ranking
1509
National Ranking
14

Research.com Recognitions

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

Overview

Nico Karssemeijer is affiliated with Radboud University in the Netherlands and has an extensive research portfolio primarily focused on medical imaging, cancer detection, and artificial intelligence applications in healthcare. Their work spans multiple interdisciplinary fields, including medicine and computer science, with a strong emphasis on radiology and oncology.

Their main fields of study include:

  • Medicine
  • Computer Science

Within these broader fields, their research targets several subfields, notably:

  • Radiology, Nuclear Medicine and Imaging
  • Artificial Intelligence
  • Pulmonary and Respiratory Medicine
  • Oncology
  • Pathology and Forensic Medicine

Karssemeijer's published works cover key topics such as:

  • Radiomics and Machine Learning in Medical Imaging
  • AI in cancer detection
  • Digital Radiography and Breast Imaging
  • MRI in cancer diagnosis
  • Global Cancer Incidence and Screening
  • Breast Lesions and Carcinomas
  • Breast Cancer Treatment Studies

Among frequently published venues, the majority of their research appears in journals including:

  • Radiology
  • European Radiology
  • arXiv (Cornell University)
  • Insights into Imaging
  • European Journal of Surgical Oncology

Key recent publications include:

  • "Supplemental Breast MRI for Women with Extremely Dense Breasts: Results of the Second Screening Round of the DENSE Trial" (2021, Radiology)
  • "An Artificial Intelligence-based Mammography Screening Protocol for Breast Cancer: Outcome and Radiologist Workload" (2022, Radiology)
  • "Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study" (2021, European Radiology)
  • "Interval Cancer Detection Using a Neural Network and Breast Density in Women with Negative Screening Mammograms" (2022, Radiology)
  • "Early Indicators of the Impact of Using AI in Mammography Screening for Breast Cancer" (2024, Radiology)

Karssemeijer regularly collaborates with several researchers, including:

  • Ritse M. Mann
  • Marc B. I. Lobbes
  • Carla H. van Gils
  • Jonas Teuwen
  • Stefanie G. A. Veenhuizen

Their contributions notably address screening protocols, the integration of artificial intelligence for improved diagnostic accuracy, and efforts to optimize radiologist workload in breast cancer detection. This work bridges clinical radiology and advanced computational methods, supporting innovations in early cancer diagnosis and medical imaging technologies.

Best Publications

  • 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

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

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

  • Computer-Aided Detection of Prostate Cancer in MRI

    Geert Litjens;Oscar Debats;Jelle Barentsz;Nico Karssemeijer

  • Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring

    Michiel Kallenberg;Kersten Petersen;Mads Nielsen;Andrew Y. Ng

  • Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks

    David Tellez;Maschenka Balkenhol;Irene Otte-Holler;Rob van de Loo

  • Detection of stellate distortions in mammograms

    N. Karssemeijer;G.M. te Brake

  • Automated classification of parenchymal patterns in mammograms

    N Karssemeijer

  • Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation

    Mohsen Ghafoorian;Mohsen Ghafoorian;Alireza Mehrtash;Alireza Mehrtash;Tina Kapur;Nico Karssemeijer

  • Stain Specific Standardization of Whole-Slide Histopathological Images

    Babak Ehteshami Bejnordi;Geert Litjens;Nadya Timofeeva;Irene Otte-Holler

  • Volumetric breast density estimation from full-field digital mammograms

    S. van Engeland;P.R. Snoeren;H. Huisman;C. Boetes

  • Robust breast composition measurement - Volpara™

    Ralph Highnam;Sir Michael Brady;Martin J. Yaffe;Nico Karssemeijer

  • Guest editorial computer-aided diagnosis in medical imaging

    M.L. Giger;N. Karssemeijer;S.G. Armato

  • Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities.

    Mohsen Ghafoorian;Nico Karssemeijer;Tom Heskes;Inge W. M. van Uden

  • Breast Image Analysis for Risk Assessment, Detection, Diagnosis, and Treatment of Cancer

    Maryellen L Giger;Nico Karssemeijer;Julia A Schnabel

  • A novel approach to contrast-enhanced breast magnetic resonance imaging for screening: high-resolution ultrafast dynamic imaging.

    Ritse M. Mann;Roel D. Mus;Jan van Zelst;Christian Geppert

  • A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography.

    Sheila Timp;Nico Karssemeijer

  • Using deep learning to segment breast and fibroglandular tissue in MRI volumes

    Mehmet Ufuk Dalmış;Geert Litjens;Katharina Holland;Arnaud Setio

  • ADAPTIVE NOISE EQUALIZATION AND RECOGNITION OF MICROCALCIFICATION CLUSTERS IN MAMMOGRAMS

    Nico Karssemeijer

  • Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies.

    Babak Ehteshami Bejnordi;Babak Ehteshami Bejnordi;Maeve Mullooly;Ruth M. Pfeiffer;Shaoqi Fan

  • Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images

    Babak Ehteshami Bejnordi;Guido C. A. Zuidhof;Maschenka Balkenhol;Meyke Hermsen

  • Single and multiscale detection of masses in digital mammograms

    G.M. te Brake;N. Karssemeijer

Frequent Co-Authors

Geert Litjens
Geert Litjens Radboud University
Bram van Ginneken
Bram van Ginneken Radboud University
Mads Nielsen
Mads Nielsen University of Copenhagen
Peter J. F. Lucas
Peter J. F. Lucas University of Twente
David J. Hawkes
David J. Hawkes University College London
Elena Marchiori
Elena Marchiori Radboud University
Tom Heskes
Tom Heskes Radboud University
Boudewijn P. F. Lelieveldt
Boudewijn P. F. Lelieveldt Leiden University Medical Center
Clara I. Sánchez
Clara I. Sánchez University of Amsterdam

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