Publications of Geert Litjens

2019

Papers in international journals

  1. P. Bándi, M. Balkenhol, B. van Ginneken, J. van der Laak and G. Litjens, "Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks", PeerJ, 2019;7:e8242.
    Abstract DOI PMID Cited by ~44
  2. O. Debats, G. Litjens and H. Huisman, "Lymph node detection in MR Lymphography: false positive reduction using multi-view convolutional neural networks", PeerJ, 2019;7:e8052.
    Abstract DOI PMID Download Cited by ~14
  3. J. van der Laak, F. Ciompi and G. Litjens, "No pixel-level annotations needed", Nature Biomedical Engineering, 2019;3(11):855-856.
    Abstract DOI PMID Download Cited by ~13
  4. Z. Swiderska-Chadaj, H. Pinckaers, M. van Rijthoven, M. Balkenhol, M. Melnikova, O. Geessink, Q. Manson, M. Sherman, A. Polonia, J. Parry, M. Abubakar, G. Litjens, J. van der Laak and F. Ciompi, "Learning to detect lymphocytes in immunohistochemistry with deep learning", Medical Image Analysis, 2019;58:101547.
    Abstract DOI PMID Cited by ~106
  5. D. Tellez, G. Litjens, P. Bándi, W. Bulten, J. Bokhorst, F. Ciompi and J. van der Laak, "Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology", Medical Image Analysis, 2019;58:101544.
    Abstract DOI PMID Cited by ~385
  6. G. Litjens, F. Ciompi, J. Wolterink, B. de Vos, T. Leiner, J. Teuwen and I. Isgum, "State-of-the-Art Deep Learning in Cardiovascular Image Analysis", JACC Cardiovascular Imaging, 2019;12(8 Pt 1):1549-1565.
    Abstract DOI PMID Download Cited by ~246
  7. L. Aprupe, G. Litjens, T. Brinker, J. van der Laak and N. Grabe, "Robust and accurate quantification of biomarkers of immune cells in lung cancer micro-environment using deep convolutional neural networks", PeerJ, 2019;7:e6335.
    Abstract DOI PMID Download Cited by ~23
  8. M. Maas, G. Litjens, A. Wright, U. Attenberger, M. Haider, T. Helbich, B. Kiefer, K. Macura, D. Margolis, A. Padhani, K. Selnaes, G. Villeirs, J. Futterer and T. Scheenen, "A Single-Arm, Multicenter Validation Study of Prostate Cancer Localization and Aggressiveness With a Quantitative Multiparametric Magnetic Resonance Imaging Approach", Investigative Radiology, 2019.
    Abstract DOI PMID Cited by ~23
  9. O. Geessink, A. Baidoshvili, J. Klaase, B. Ehteshami Bejnordi, G. Litjens, G. van Pelt, W. Mesker, I. Nagtegaal, F. Ciompi and J. van der Laak, "Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer", Cellular Oncology, 2019:1-11.
    Abstract DOI PMID Download Cited by ~77
  10. W. Bulten, P. Bándi, J. Hoven, R. van de Loo, J. Lotz, N. Weiss, J. van der Laak, B. van Ginneken, C. Hulsbergen-van de Kaa and G. Litjens, "Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard", Scientific Reports, 2019;9(1).
    Abstract DOI PMID arXiv Cited by ~128

Preprints

  1. H. Pinckaers and G. Litjens, "Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands", arXiv:1910.10470, 2019.
    Abstract DOI arXiv Cited by ~28
  2. A. Simpson, M. Antonelli, S. Bakas, M. Bilello, K. Farahani, B. van Ginneken, A. Kopp-Schneider, B. Landman, G. Litjens, B. Menze, O. Ronneberger, R. Summers, P. Bilic, P. Christ, R. Do, M. Gollub, J. Golia-Pernicka, S. Heckers, W. Jarnagin, M. McHugo, S. Napel, E. Vorontsov, L. Maier-Hein and M. Cardoso, "A large annotated medical image dataset for the development and evaluation of segmentation algorithms", arXiv:1902.09063, 2019.
    Abstract arXiv Cited by ~665

Papers in conference proceedings

  1. T. de Bel, M. Hermsen, J. Kers, J. van der Laak and G. Litjens, "Stain-Transforming Cycle-Consistent Generative Adversarial Networks for Improved Segmentation of Renal Histopathology", Medical Imaging with Deep Learning, 2019.
    Abstract Url Cited by ~75
  2. K. Dercksen, W. Bulten and G. Litjens, "Dealing with Label Scarcity in Computational Pathology: A Use Case in Prostate Cancer Classification", Medical Imaging with Deep Learning, 2019.
    Abstract Url Cited by ~6
  3. H. Pinckaers, W. Bulten and G. Litjens, "High resolution whole prostate biopsy classification using streaming stochastic gradient descent", Medical Imaging, 2019(1).
    Abstract DOI Cited by ~3

Abstracts

  1. W. Bulten, H. Pinckaers, C. Hulsbergen-van de Kaa and G. Litjens, "Automated Gleason Grading of Prostate Biopsies Using Deep Learning", United States and Canadian Academy of Pathology (USCAP) 108th Annual Meeting, 2019.
    Abstract Cited by ~122