Publications of Nico Karssemeijer

all all years 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 and before

2018

Papers in international journals

  1. S. Vreemann, J. van Zelst, M. Schlooz-Vries, P. Bult, N. Hoogerbrugge, N. Karssemeijer, A. Gubern-Merida and R. Mann, "The added value of mammography in different age-groups of women with and without BRCA mutation screened with breast MRI", Breast Cancer Research, 2018;20(1):84.
    Abstract DOI PMID Cited by ~35
  2. D. Tellez, M. Balkenhol, I. Otte-Holler, R. van de Loo, R. Vogels, P. Bult, C. Wauters, W. Vreuls, S. Mol, N. Karssemeijer, G. Litjens, J. van der Laak and F. Ciompi, "Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks", IEEE Transactions on Medical Imaging, 2018;37(9):2126 - 2136.
    Abstract DOI PMID Cited by ~200
  3. A. Bria, C. Marrocco, L. Borges, M. Molinara, A. Marchesi, J. Mordang, N. Karssemeijer and F. Tortorella, "Improving the Automated Detection of Calcifications using Adaptive Variance Stabilization", IEEE Transactions on Medical Imaging, 2018;37(8):1857-1864.
    Abstract DOI PMID Cited by ~10
  4. J. van Zelst, S. Vreemann, H. Witt, A. Gubern-Merida, M. Dorrius, K. Duvivier, S. Lardenoije-Broker, M. Lobbes, C. Loo, W. Veldhuis, J. Veltman, D. Drieling, N. Karssemeijer and R. Mann, "Multireader Study on the Diagnostic Accuracy of Ultrafast Breast Magnetic Resonance Imaging for Breast Cancer Screening", Investigative Radiology, 2018;53(10):579-586.
    Abstract DOI PMID Cited by ~48
  5. B. Ehteshami Bejnordi, M. Mullooly, R. Pfeiffer, S. Fan, P. Vacek, D. Weaver, S. Herschorn, L. Brinton, B. van Ginneken, N. Karssemeijer, A. Beck, G. Gierach, J. van der Laak and M. Sherman, "Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies", Modern Pathology, 2018;31(10):1502-1512.
    Abstract DOI PMID Cited by ~150
  6. S. de Lange, M. Bakker, E. Monninkhof, P. Peeters, P. de Koekkoek-Doll, R. Mann, M. Rutten, R. Bisschops, J. Veltman, K. Duvivier, M. Lobbes, H. de Koning, N. Karssemeijer, R. Pijnappel, W. Veldhuis and C. van Gils, "Reasons for (non)participation in supplemental population-based MRI breast screening for women with extremely dense breasts", Clinical Radiology, 2018;73(8):759e1-759e9.
    Abstract DOI PMID Cited by ~21
  7. J. Wanders, C. van Gils, N. Karssemeijer, K. Holland, M. Kallenberg, P. Peeters, M. Nielsen and M. Lillholm, "The combined effect of mammographic texture and density on breast cancer risk: a cohort study", Breast Cancer Research, 2018;20.
    Abstract DOI PMID Cited by ~28
  8. J. van Zelst, T. Tan, P. Clauser, A. Domingo, M. Dorrius, D. Drieling, M. Golatta, F. Gras, M. de Jong, R. Pijnappel, M. Rutten, N. Karssemeijer and R. Mann, "Dedicated computer-aided detection software for automated 3D breast ultrasound; an efficient tool for the radiologist in supplemental screening of women with dense breasts", European Radiology, 2018;28(7):2996-3006.
    Abstract DOI PMID Cited by ~52
  9. S. Vreemann, A. Gubern-Merida, S. Lardenoije, P. Bult, N. Karssemeijer, K. Pinker and R. Mann, "The frequency of missed breast cancers in women participating in a high-risk MRI screening program", Breast Cancer Research and Treatment, 2018;169(2):323-331.
    Abstract DOI PMID Cited by ~25
  10. S. Vreemann, A. Gubern-Mérida, C. Borelli, P. Bult, N. Karssemeijer and R. Mann, "The correlation of background parenchymal enhancement in the contralateral breast with patient and tumor characteristics of MRI-screen detected breast cancers", PLoS One, 2018;13(1):e0191399.
    Abstract DOI PMID Cited by ~17
  11. M. Dalmis, S. Vreemann, T. Kooi, R. Mann, N. Karssemeijer and A. Gubern-Merida, "Fully automated detection of breast cancer in screening MRI using convolutional neural networks", Journal of Medical Imaging, 2018;5(1):014502.
    Abstract DOI PMID Cited by ~54
  12. A. Rodriguez-Ruiz, J. Teuwen, S. Vreemann, R. Bouwman, R. van Engen, N. Karssemeijer, R. Mann, A. Gubern-Merida and I. Sechopoulos, "New reconstruction algorithm for digital breast tomosynthesis: better image quality for humans and computers", Acta Radiologica, 2018;59(9):1051-1059.
    Abstract DOI PMID Cited by ~33
  13. A. Rodriguez-Ruiz, A. Gubern-Merida, M. Imhof-Tas, S. Lardenoije, A. Wanders, I. Andersson, S. Zackrisson, K. Lang, M. Dustler, N. Karssemeijer, R. Mann and I. Sechopoulos, "One-view digital breast tomosynthesis as a stand-alone modality for breast cancer detection: do we need more?", European Radiology, 2018;28(5):1938-1948.
    Abstract DOI PMID Cited by ~27
  14. C. Balta, R. Bouwman, I. Sechopoulos, M. Broeders, N. Karssemeijer, R. van Engen and W. Veldkamp, "A model observer study using acquired mammographic images of an anthropomorphic breast phantom", Medical Physics, 2018;45(2):655-665.
    Abstract DOI PMID Cited by ~15
  15. J. Mordang, A. Gubern-Merida, A. Bria, F. Tortorella, R. Mann, M. Broeders, G. den Heeten and N. Karssemeijer, "The importance of early detection of calcifications associated with breast cancer in screening", Breast Cancer Research and Treatment, 2018;167(2):451-458.
    Abstract DOI PMID Cited by ~12
  16. S. Vreemann, A. Gubern-Merida, M. Schlooz-Vries, P. Bult, C. van Gils, N. Hoogerbrugge, N. Karssemeijer and R. Mann, "Influence of Risk Category and Screening Round on the Performance of an MR Imaging and Mammography Screening Program in Carriers of the BRCA Mutation and Other Women at Increased Risk", Radiology, 2018;286(2):443-451.
    Abstract DOI PMID Cited by ~45

Papers in conference proceedings

  1. A. Bria, B. Savelli, C. Marrocco, J. Mordang, M. Molinara, N. Karssemeijer and F. Tortorella, "Improving the automated detection of calcifications by combining deep cascades and deep convolutional nets", 14th International Workshop on Breast Imaging (IWBI 2018), 2018.
    Abstract DOI Cited by ~6
  2. A. Rodriguez-Ruiz, R. van Engen, K. Michielsen, R. Bouwman, S. Vreemann, N. Karssemeijer, R. Mann and I. Sechopoulos, "How does wide-angle breast tomosynthesis depict calcifications in comparison to digital mammography? A retrospective observer study", 14th International Workshop on Breast Imaging (IWBI 2018), 2018.
    Abstract DOI Cited by ~2
  3. M. Ghafoorian, J. Teuwen, R. Manniesing, F. de Leeuw, B. van Ginneken, N. Karssemeijer and B. Platel, "Student Beats the Teacher: Deep Neural Networks for Lateral Ventricles Segmentation in Brain MR", Medical Imaging, 2018;10574:105742U.
    Abstract DOI arXiv Cited by ~18
  4. D. Tellez, M. Balkenhol, N. Karssemeijer, G. Litjens, J. van der Laak and F. Ciompi, "H&E stain augmentation improves generalization of convolutional networks for histopathological mitosis detection", Medical Imaging, 2018;10581.
    Abstract DOI Cited by ~43
  5. C. Marrocco, A. Bria, V. Di Sano, L. Borges, B. Savelli, M. Molinara, J. Mordang, N. Karssemeijer and F. Tortorella, "Mammogram denoising to improve the calcification detection performance of convolutional nets", 14th International Workshop on Breast Imaging (IWBI 2018), 2018.
    Abstract DOI Cited by ~6
  6. A. Rodriguez-Ruiz, J. Teuwen, K. Chung, N. Karssemeijer, M. Chevalier, A. Gubern-Merida and I. Sechopoulos, "Pectoral muscle segmentation in breast tomosynthesis with deep learning", Medical Imaging, 2018.
    Abstract DOI Cited by ~21
  7. A. Rodriguez-Ruiz, J. Mordang, N. Karssemeijer, I. Sechopoulos and R. Mann, "Can radiologists improve their breast cancer detection in mammography when using a deep learning based computer system as decision support?", 14th International Workshop on Breast Imaging (IWBI 2018), 2018.
    Abstract DOI Cited by ~13

PhD theses

  1. J. Mordang, "Towards an independent observer of screening mammograms: detection of calcifications", PhD thesis, 2018.
    Abstract Url
  2. S. Vreemann, "Breast MRI for screening: evaluation of clinical practice and future perspectives", PhD thesis, 2018.
    Abstract Url
  3. T. Kooi, "Computer aided diagnosis of breast cancer in mammography using deep neural networks", PhD thesis, 2018.
    Abstract Url
  4. M. Ghafoorian, "Machine Learning for Quantification of Small Vessel Disease Imaging Biomarkers", PhD thesis, 2018.
    Abstract Url