Publications of Jeroen van der Laak

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. J. Bokhorst, A. Blank, A. Lugli, I. Zlobec, H. Dawson, M. Vieth, L. Rijstenberg, S. Brockmoeller, M. Urbanowicz, J. Flejou, R. Kirsch, F. Ciompi, J. van der Laak and I. Nagtegaal, "Assessment of individual tumor buds using keratin immunohistochemistry: moderate interobserver agreement suggests a role for machine learning", Modern Pathology, 2019.
   Abstract DOI PMID Cited by ~32
3. M. Mullooly, B. Ehteshami Bejnordi, R. Pfeiffer, S. Fan, M. Palakal, M. Hada, P. Vacek, D. Weaver, J. Shepherd, B. Fan, A. Mahmoudzadeh, J. Wang, S. Malkov, J. Johnson, S. Herschorn, B. Sprague, S. Hewitt, L. Brinton, N. Karssemeijer, J. van der Laak, A. Beck, M. Sherman and G. Gierach, "Application of convolutional neural networks to breast biopsies to delineate tissue correlates of mammographic breast density", NPJ Breast Cancer, 2019;5:43.
   Abstract DOI PMID Download Cited by ~14
4. 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
5. M. Hermsen, T. de Bel, M. den Boer, E. Steenbergen, J. Kers, S. Florquin, J. Roelofs, M. Stegall, M. Alexander, B. Smith, B. Smeets, L. Hilbrands and J. van der Laak, "Deep-learning based histopathologic assessment of kidney tissue", Journal of the American Society of Nephrology, 2019;30(10):1968-1979.
   Abstract DOI PMID Cited by ~215
6. 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
7. 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
8. A. Halilovic, D. Verweij, A. Simons, M. Stevens-Kroef, S. Vermeulen, J. Elsink, B. Tops, I. Otte-Holler, J. van der Laak, C. van de Water, O. Boelens, M. Schlooz-Vries, J. Dijkstra, I. Nagtegaal, J. Tol, P. van Cleef, P. Span and P. Bult, "HER2, chromosome 17 polysomy and DNA ploidy status in breast cancer; a translational study", Scientific Reports, 2019;9(1):11679.
   Abstract DOI PMID Download Cited by ~15
9. E. Abels, L. Pantanowitz, F. Aeffner, M. Zarella, J. van der Laak, M. Bui, V. Vemuri, A. Parwani, J. Gibbs, E. Agosto-Arroyo, A. Beck and C. Kozlowski, "Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association", Journal of Pathology, 2019;249(3):286-294.
   Abstract DOI PMID Download Cited by ~24
10. M. Balkenhol, D. Tellez, W. Vreuls, P. Clahsen, H. Pinckaers, F. Ciompi, P. Bult and J. van der Laak, "Deep learning assisted mitotic counting for breast cancer", Laboratory Investigation, 2019.
    Abstract DOI PMID Cited by ~72
11. I. Munsterman, M. Van Erp, G. Weijers, C. Bronkhorst, C. de Korte, J. Drenth, J. van der Laak and E. Tjwa, "A Novel Automatic Digital Algorithm that Accurately Quantifies Steatosis in NAFLD on Histopathological Whole-Slide Images", Cytometry Part B-Clinical Cytometry, 2019.
    Abstract DOI PMID Cited by ~27
12. 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
13. M. Balkenhol, P. Bult, D. Tellez, W. Vreuls, P. Clahsen, F. Ciompi and J. van der Laak, "Deep learning and manual assessment show that the absolute mitotic count does not contain prognostic information in triple negative breast cancer", Cellular Oncology, 2019;42:4555-4569.
    Abstract DOI PMID Download Cited by ~20
14. B. Sturm, D. Creytens, M. Cook, J. Smits, M. van Dijk, E. Eijken, E. Kurpershoek, H. Kusters-Vandevelde, A. Ooms, C. Wauters, W. Blokx and J. van der Laak, "Validation of Whole-slide Digitally Imaged Melanocytic Lesions: Does Z-Stack Scanning Improve Diagnostic Accuracy?", Journal of Pathology Informatics, 2019;10:6.
    Abstract DOI PMID Cited by ~4
15. 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
16. 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

Papers in conference proceedings

1. C. Mercan, M. Balkenhol, J. van der Laak and F. Ciompi, "From Point Annotations to Epithelial Cell Detection in Breast Cancer Histopathology using RetinaNet", Medical Imaging with Deep Learning, 2019.
   Abstract Url Cited by ~6
2. 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
3. J. Bokhorst, H. Pinckaers, P. van Zwam, I. Nagetgaal, J. van der Laak and F. Ciompi, "Learning from sparsely annotated data for semantic segmentation in histopathology images", Medical Imaging with Deep Learning, 2019;102:81-94.
   Abstract Url Cited by ~36

Abstracts

1. T. Haddad, N. Farahani, J. Bokhorst, F. Doubrava-Simmer, F. Ciompi, I. Nagtegaal and J. van der Laak, "A Colorectal Carcinoma in 3D: Merging Knife-Edge Scanning Microscopy and Deep Learning", EACR, 2019.
   Abstract
2. W. Aswolinskiy, H. Horlings, L. Mulder, J. van der Laak, J. Wesseling, E. Lips and F. Ciompi, "Potential of an AI-based digital biomarker to predict neoadjuvant chemotherapy response from preoperative biopsies of Luminal-B breast cancer", European Congress of Pathology, 2019.
   Abstract
3. J. Bokhorst, H. Dawson, A. Blank, I. Zlobec, A. Lugli, M. Vieth, R. Kirsch, M. Urbanowicz, S. Brockmoeller, J. Flejou, L. Rijstenberg, J. van der Laak, F. Ciompi and I. Nagtegaal, "Assessment of tumor buds in colorectal cancer. A large-scale international digital observer study", European Congress of Pathology, 2019.
   Abstract
4. M. Hermsen, T. de Bel, M. den Boer, E. Steenbergen, J. Kers, S. Florquin, J. Roelofs, M. Stegall, M. Alexander, B. Smith, B. Smeets, L. Hilbrands and J. van der Laak, "Deep learning-based histopathological assessment of renal tissue", American Society of Nephrology Kidney Week 2019, 2019.
   Abstract