Publications of Nico Karssemeijer

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

2017

Papers in international journals

  1. B. Bejnordi, G. Zuidhof, M. Balkenhol, M. Hermsen, P. Bult, B. van Ginneken, N. Karssemeijer, G. Litjens and J. van der Laak, "Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images", Journal of Medical Imaging, 2017;4(4):044504.
    Abstract DOI PMID Download Cited by ~150
  2. B. Ehteshami Bejnordi, M. Veta, P. van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. van der Laak, T. Consortium, M. Hermsen, Q. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. van Dijk, P. Bult, F. Beca, A. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H. Lin, P. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu and R. Venâncio, "Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer", Journal of the American Medical Association, 2017;318(22):2199-2210.
    Abstract DOI PMID Cited by ~1000
  3. K. Holland, I. Sechopoulos, R. Mann, G. den Heeten, C. van Gils and N. Karssemeijer, "Influence of breast compression pressure on the performance of population-based mammography screening", Breast Cancer Research, 2017;19(1):126.
    Abstract DOI PMID Cited by ~38
  4. T. Kooi and N. Karssemeijer, "Classifying symmetrical differences and temporal change for the detection of malignant masses in mammography using deep neural networks", Journal of Medical Imaging, 2017;4(4):International Society for Optics and Photonics.
    Abstract DOI PMID Cited by ~45
  5. E. Gray, A. Donten, N. Karssemeijer, C. van Gils, D. Evans, S. Astley and K. Payne, "Evaluation of a Stratified National Breast Screening Program in the United Kingdom: An Early Model-Based Cost-Effectiveness Analysis", Value in Health, 2017;20:1100-1109.
    Abstract DOI PMID Cited by ~49
  6. M. Ghafoorian, N. Karssemeijer, T. Heskes, I. van Uden, C. Sánchez, G. Litjens, F. de Leeuw, B. van Ginneken, E. Marchiori and B. Platel, "Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities", Scientific Reports, 2017;7(1):5110.
    Abstract DOI PMID arXiv Cited by ~211
  7. J. van Zelst, R. Mus, G. Woldringh, M. Rutten, P. Bult, S. Vreemann, M. de Jong, N. Karssemeijer, N. Hoogerbrugge and R. Mann, "Surveillance of Women with the BRCA1 or BRCA2 Mutation by Using Biannual Automated Breast US, MR Imaging, and Mammography", Radiology, 2017;285(2):376-388.
    Abstract DOI PMID Cited by ~55
  8. J. Wanders, K. Holland, N. Karssemeijer, P. Peeters, W. Veldhuis, R. Mann and C. van Gils, "The effect of volumetric breast density on the risk of screen-detected and interval breast cancers: a cohort study", Breast Cancer Research, 2017;19(1):67.
    Abstract DOI PMID Cited by ~57
  9. J. van Zelst, M. Balkenhol, T. Tan, M. Rutten, M. Imhof-Tas, P. Bult, N. Karssemeijer and R. Mann, "Sonographic Phenotypes of Molecular Subtypes of Invasive Ductal Cancer in Automated 3-D Breast Ultrasound", Ultrasound in Medicine and Biology, 2017;43(9):1820-1828.
    Abstract DOI PMID Cited by ~9
  10. S. Vreemann, A. Rodriguez-Ruiz, D. Nickel, L. Heacock, L. Appelman, J. van Zelst, N. Karssemeijer, E. Weiland, M. Maas, L. Moy, B. Kiefer and R. Mann, "Compressed Sensing for Breast MRI: Resolving the Trade-Off Between Spatial and Temporal Resolution", Investigative Radiology, 2017;52(10):574-582.
    Abstract DOI PMID Cited by ~41
  11. M. Ghafoorian, N. Karssemeijer, T. Heskes, M. Bergkamp, J. Wissink, J. Obels, K. Keizer, F. de Leeuw, B. Ginneken, E. Marchiori and B. Platel, "Deep multi-scale location-aware 3D convolutional neural networks for automated detection of lacunes of presumed vascular origin", NeuroImage: Clinical, 2017;14:391-399.
    Abstract DOI PMID Cited by ~117
  12. R. Mus, C. Borelli, P. Bult, E. Weiland, N. Karssemeijer, J. Barentsz, A. Gubern-Mérida, B. Platel and R. Mann, "Time to enhancement derived from ultrafast breast MRI as a novel parameter to discriminate benign from malignant breast lesions", European Journal of Radiology, 2017;89:90-96.
    Abstract DOI PMID Cited by ~71
  13. J. van Zelst, T. Tan, B. Platel, M. de Jong, A. Steenbakkers, M. Mourits, A. Grivegnee, C. Borelli, N. Karssemeijer and R. Mann, "Improved cancer detection in automated breast ultrasound by radiologists using Computer Aided Detection", European Journal of Radiology, 2017;89:54-59.
    Abstract DOI PMID Cited by ~47
  14. K. Holland, A. Gubern-Mérida, R. Mann and N. Karssemeijer, "Optimization of volumetric breast density estimation in digital mammograms", Physics in Medicine and Biology, 2017;62(9):3779-3797.
    Abstract DOI PMID Cited by ~6
  15. J. Mordang, A. Gubern-Merida, A. Bria, F. Tortorella, G. den Heeten and N. Karssemeijer, "Improving computer-aided detection assistance in breast cancer screening by removal of obviously false-positive findings", Medical Physics, 2017;44(4):1390-1401.
    Abstract DOI PMID Cited by ~17
  16. K. Holland, C. van Gils, R. Mann and N. Karssemeijer, "Quantification of masking risk in screening mammography with volumetric breast density maps", Breast Cancer Research and Treatment, 2017;162(3):541-548.
    Abstract DOI PMID Cited by ~33
  17. T. Kooi, B. van Ginneken, N. Karssemeijer and A. den Heeten, "Discriminating solitary cysts from soft tissue lesions in mammography using a pretrained deep convolutional neural network", Medical Physics, 2017;44(3):1017-1027.
    Abstract DOI PMID Download Cited by ~97
  18. T. Mertzanidou, J. Hipwell, S. Reis, D. Hawkes, B. Bejnordi, M. Dalmis, S. Vreemann, B. Platel, J. van der Laak, N. Karssemeijer, M. Hermsen, P. Bult and R. Mann, "3D volume reconstruction from serial breast specimen radiographs for mapping between histology and 3D whole specimen imaging", Medical Physics, 2017;44(3):935-948.
    Abstract DOI PMID Download Cited by ~18
  19. M. Dalmis, G. Litjens, K. Holland, A. Setio, R. Mann, N. Karssemeijer and A. Gubern-Mérida, "Using deep learning to segment breast and fibroglandular tissue in MRI volumes", Medical Physics, 2017;44(2):533-546.
    Abstract DOI PMID Download Cited by ~180
  20. J. Wanders, K. Holland, W. Veldhuis, R. Mann, R. Pijnappel, P. Peeters, C. van Gils and N. Karssemeijer, "Volumetric breast density affects performance of digital screening mammography", Breast Cancer Research and Treatment, 2017;162(1):95-103.
    Abstract DOI PMID Cited by ~108
  21. T. Kooi, G. Litjens, B. van Ginneken, A. Gubern-Mérida, C. Sánchez, R. Mann, A. den Heeten and N. Karssemeijer, "Large scale deep learning for computer aided detection of mammographic lesions", Medical Image Analysis, 2017;35:303-312.
    Abstract DOI PMID Download Cited by ~774

Preprints

  1. T. Kooi and N. Karssemeijer, "Classifying Symmetrical Differences and Temporal Change in Mammography Using Deep Neural Networks", arXiv:1703.07715, 2017.
    Abstract DOI arXiv Cited by ~11

Papers in conference proceedings

  1. B. Bejnordi, J. Lin, B. Glass, M. Mullooly, G. Gierach, M. Sherman, N. Karssemeijer, J. van der Laak and A. Beck, "Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images", IEEE International Symposium on Biomedical Imaging, 2017:929-932.
    Abstract DOI PMID arXiv Cited by ~63
  2. M. Ghafoorian, A. Mehrtash, T. Kapur, N. Karssemeijer, E. Marchiori, M. Pesteie, C. Guttmann, F. de Leeuw, C. Tempany, B. van Ginneken, A. Fedorov, P. Abolmaesumi, B. Platel and W. Wells, "Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation", Medical Image Computing and Computer-Assisted Intervention, 2017;10435:516-524.
    Abstract DOI arXiv Cited by ~288
  3. T. Kooi, J. Mordang and N. Karssemeijer, "Conditional Random Field Modelling of Interactions Between Findings in Mammography", Medical Imaging, 2017;10133:101341E.
    Abstract DOI Cited by ~2
  4. C. Balta, R. Bouwman, I. Sechopoulos, M. Broeders, N. Karssemeijer, R. van Engen and W. Veldkamp, "Signal template generation from acquired mammographic images for the non-prewhitening model observer with eye-filter", Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment, 2017.
    Abstract DOI Cited by ~5
  5. A. Marchesi, A. Bria, C. Marrocco, M. Molinara, J. Mordang, F. Tortorella and N. Karssemeijer, "The Effect of Mammogram Preprocessing on Microcalcification Detection with Convolutional Neural Networks", 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), 2017.
    Abstract DOI Cited by ~9
  6. T. Kooi and N. Karssemeijer, "Deep learning of symmetrical discrepancies for computer-aided detection of mammographic masses", Medical Imaging, 2017;10133:101341J.
    Abstract DOI Cited by ~7

Abstracts

  1. J. Teuwen, M. Kallenberg, A. Gubern-Merida, A. Rodriguez-Ruiz, N. Karssemeijer and R. Mann, "Automated pre-selection of mammograms without abnormalities using deep learning", Annual Meeting of the Radiological Society of North America, 2017.
    Abstract

PhD theses

  1. B. Bejnordi, "Histopathological diagnosis of breast cancer using machine learning", PhD thesis, 2017.
    Abstract Url
  2. K. Holland, "Breast density measurement for personalised screening", PhD thesis, 2017.
    Abstract Url

Other publications

  1. A. Bria, C. Marrocco, A. Galdran, A. Campilho, A. Marchesi, J. Mordang, N. Karssemeijer, M. Molinara and F. Tortorella, "Spatial Enhancement by Dehazing for Detection of Microcalcifications with Convolutional Nets", Image Analysis and Processing - ICIAP 2017, 2017:288-298.
    Abstract DOI Cited by ~9