Publications of Jeroen van der Laak

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2020

Papers in international journals

  1. Z. Kos, A. Roblin, R. Kim, S. Michiels, B. Gallas, W. Chen, K. van de Vijver, S. Goel, S. Adams, S. Demaria, G. Viale, T. Nielsen, S. Badve, W. Symmans, C. Sotiriou, D. Rimm, S. Hewitt, C. Denkert, S. Loibl, S. Luen, J. Bartlett, P. Savas, G. Pruneri, D. Dillon, M. Cheang, A. Tutt, J. Hall, M. Kok, H. Horlings, A. Madabhushi, J. van der Laak, F. Ciompi, A. Laenkholm, E. Bellolio, T. Gruosso, S. Fox, J. Araya, G. Floris, J. Hudeček, L. Voorwerk, A. Beck, J. Kerner, D. Larsimont, S. Declercq, G. den Eynden, L. Pusztai, A. Ehinger, W. Yang, K. AbdulJabbar, Y. Yuan, R. Singh, C. Hiley, M. al Bakir, A. Lazar, S. Naber, S. Wienert, M. Castillo, G. Curigliano, M. Dieci, F. André, C. Swanton, J. Reis-Filho, J. Sparano, E. Balslev, I. Chen, E. Stovgaard, K. Pogue-Geile, K. Blenman, F. Penault-Llorca, S. Schnitt, S. Lakhani, A. Vincent-Salomon, F. Rojo, J. Braybrooke, M. Hanna, M. Soler-Monsó, D. Bethmann, C. Castaneda, K. Willard-Gallo, A. Sharma, H. Lien, S. Fineberg, J. Thagaard, L. Comerma, P. Gonzalez-Ericsson, E. Brogi, S. Loi, J. Saltz, F. Klaushen, L. Cooper, M. Amgad, D. Moore and R. Salgado, "Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer", npj Breast Cancer, 2020;6. Abstract/PDF DOI
  2. M. Amgad, A. Stovgaard, E. Balslev, J. Thagaard, W. Chen, S. Dudgeon, A. Sharma, J. Kerner, C. Denkert, Y. Yuan, K. AbdulJabbar, S. Wienert, P. Savas, L. Voorwerk, A. Beck, A. Madabhushi, J. Hartman, M. Sebastian, H. Horlings, J. Hudeček, F. Ciompi, D. Moore, R. Singh, E. Roblin, M. Balancin, M. Mathieu, J. Lennerz, P. Kirtani, I. Chen, J. Braybrooke, G. Pruneri, S. Demaria, S. Adams, S. Schnitt, S. Lakhani, F. Rojo, L. Comerma, S. Badve, M. Khojasteh, W. Symmans, C. Sotiriou, P. Gonzalez-Ericsson, K. Pogue-Geile, R. Kim, D. Rimm, G. Viale, S. Hewitt, J. Bartlett, F. Penault-Llorca, S. Goel, H. Lien, S. Loibl, Z. Kos, S. Loi, M. Hanna, S. Michiels, M. Kok, T. Nielsen, A. Lazar, Z. Bago-Horvath, L. Kooreman, J. van der Laak, J. Saltz, B. Gallas, U. Kurkure, M. Barnes, R. Salgado and L. Cooper, "Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group", npj Breast Cancer, 2020;6. Abstract/PDF DOI
  3. M. Hermsen, B. Smeets, L. Hilbrands and J. van der Laak, "Artificial intelligence; is there a potential role in nephropathology?", Nephrology Dialysis Transplantation, 2020. Abstract
  4. W. Bulten, M. Balkenhol, J. Belinga, A. Brilhante, A. Çakır, L. Egevad, M. Eklund, X. Farré, K. Geronatsiou, V. Molinié, G. Pereira, P. Roy, G. Saile, P. Salles, E. Schaafsma, J. Tschui, A. Vos, B. Delahunt, H. Samaratunga, D. Grignon, A. Evans, D. Berney, C. Pan, G. Kristiansen, J. Kench, J. Oxley, K. Leite, J. McKenney, P. Humphrey, S. Fine, T. Tsuzuki, M. Varma, M. Zhou, E. Comperat, D. Bostwick, K. Iczkowski, C. Magi-Galluzzi, J. Srigley, H. Takahashi, T. van der Kwast, H. van Boven, R. Vink, J. van der Laak, C. der Kaa and G. Litjens, "Artificial Intelligence Assistance Significantly Improves Gleason Grading of Prostate Biopsies by Pathologists", Modern Pathology, 2020. Abstract/PDF DOI
  5. W. Bulten, H. Pinckaers, H. van Boven, R. Vink, T. de Bel, B. van Ginneken, J. van der Laak, C. de Kaa and G. Litjens, "Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study", Lancet Oncology, 2020;21(2):233-241. Abstract/PDF DOI PMID arXiv

Papers in conference proceedings

  1. D. Tellez, D. Hoppener, C. Verhoef, D. Grunhagen, P. Nierop, M. Drozdzal, J. van der Laak and F. Ciompi, "Extending Unsupervised Neural Image Compression With Supervised Multitask Learning", Medical Imaging with Deep Learning, 2020. Abstract/PDF

Abstracts

  1. C. Mercan, M. Balkenhol, J. Laak and F. Ciompi, "Grading nuclear pleomorphism in breast cancer using deep learning", European Congress of Pathology, 2020. Abstract
  2. J. Bokhorst, F. Ciompi, I. Zlobec, A. Lugli, M. Vieth, R. Kirsch, J. van der Laak and I. Nagtegaal, "Computer-assisted hot-spot selection for tumor budding assessment in colorectal cancer", European Congress of Pathology, 2020. Abstract
  3. J. Bokhorst, I. Nagtegaal, I. Zlobec, A. Lugli, M. Vieth, R. Kirsch, J. van der Laak and F. Ciompi, "Deep learning based tumor bud detection in pan-cytokeratin stained colorectal cancer whole-slide images", European Congress of Pathology, 2020. Abstract

2019

Papers in international journals

  1. 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/PDF DOI PMID
  2. J. van der Laak, F. Ciompi and G. Litjens, "No pixel-level annotations needed", Nature Biomedical Engineering, 2019;3(11):855-856. Abstract/PDF DOI PMID
  3. M. Mullooly, B. 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/PDF DOI PMID
  4. I. Munsterman, M. 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/PDF DOI PMID
  5. 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/PDF DOI PMID
  6. 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/PDF DOI PMID
  7. 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/PDF DOI PMID
  8. 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/PDF DOI PMID
  9. D. Tellez, G. Litjens, J. van der Laak and F. Ciompi, "Neural Image Compression for Gigapixel Histopathology Image Analysis", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019;58:101544. Abstract/PDF DOI PMID
  10. 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/PDF DOI PMID
  11. 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/PDF DOI PMID
  12. D. Tellez, G. Litjens, P. Bandi, 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/PDF DOI PMID
  13. 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/PDF DOI PMID
  14. 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", Nature Scientific Reports, 2019;9(1). Abstract/PDF DOI PMID arXiv
  15. 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/PDF DOI PMID
  16. O. Geessink, A. Baidoshvili, J. Klaase, B. 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/PDF DOI PMID
  17. 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/PDF DOI PMID

Papers in conference proceedings

  1. 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/PDF
  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/PDF
  3. 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/PDF

Abstracts

  1. 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

2018

Papers in international journals

  1. B. Bejnordi, G. Litjens and J. van der Laak, "Machine Learning Compared With Pathologist Assessment-Reply", Journal of the American Medical Association, 2018;319(16):1726. Abstract/PDF DOI PMID
  2. B. 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;31:1502-1512. Abstract/PDF DOI PMID
  3. P. Bándi, O. Geessink, Q. Manson, M. van Dijk, M. Balkenhol, M. Hermsen, B. Bejnordi, B. Lee, K. Paeng, A. Zhong, Q. Li, F. Zanjani, S. Zinger, K. Fukuta, D. Komura, V. Ovtcharov, S. Cheng, S. Zeng, J. Thagaard, A. Dahl, H. Lin, H. Chen, L. Jacobsson, M. Hedlund, M. Cetin, E. Halici, H. Jackson, R. Chen, F. Both, J. Franke, H. Kusters-Vandevelde, W. Vreuls, P. Bult, B. van Ginneken, J. van der Laak and G. Litjens, "From detection of individual metastases to classification of lymph node status at the patient level: the CAMELYON17 challenge", IEEE Transactions on Medical Imaging, 2018;38(2):550-560. Abstract/PDF DOI PMID
  4. 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;37:2126 - 2136. Abstract/PDF DOI PMID
  5. C. Reijnen, H. Kusters-Vandevelde, K. Abbink, P. Zusterzeel, A. van Herwaarden, J. van der Laak, L. Massuger, M. Snijders, J. Pijnenborg and J. Bulten, "Quantification of Leydig cells and stromal hyperplasia in the postmenopausal ovary of women with endometrial carcinoma", Human Pathology, 2018. Abstract/PDF DOI PMID
  6. A. Baidoshvili, A. Bucur, J. van Leeuwen, J. van der Laak, P. Kluin and P. van Diest, "Evaluating the benefits of digital pathology implementation: time savings in laboratory logistics", Histopathology, 2018;73(5):784-794. Abstract/PDF DOI PMID
  7. A. Baidoshvili, N. Stathonikos, G. Freling, J. Bart, N. 't Hart, J. van der Laak, J. Doff, B. van der Vegt, M. Philip and P. van Dies, "Validation of a whole-slide image-based teleconsultation network", Histopathology, 2018;73:777-783. Abstract/PDF DOI PMID
  8. G. Litjens, P. Bandi, B. Bejnordi, O. Geessink, M. Balkenhol, P. Bult, A. Halilovic, M. Hermsen, R. van de Loo, R. Vogels, Q. Manson, N. Stathonikos, A. Baidoshvili, P. van Diest, C. Wauters, M. van Dijk and J. van der Laak, "1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset", GigaScience, 2018;7:1-8. Abstract/PDF DOI PMID

Papers in conference proceedings

  1. 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/PDF DOI
  2. Z. Swiderska-Chadaj, H. Pinckaers, M. van Rijthoven, M. Balkenhol, M. Melnikova, O. Geessink, Q. Manson, G. Litjens, J. van der Laak and F. Ciompi, "Convolutional Neural Networks for Lymphocyte detection in Immunohistochemically Stained Whole-Slide Images", Medical Imaging with Deep Learning, 2018. Abstract/PDF
  3. W. Bulten, C. de Kaa, J. van der Laak and G. Litjens, "Automated segmentation of epithelial tissue in prostatectomy slides using deep learning", Medical Imaging, 2018;10581:105810S. Abstract/PDF DOI
  4. M. van Rijthoven, Z. Swiderska-Chadaj, K. Seeliger, J. van der Laak and F. Ciompi, "You Only Look on Lymphocytes Once", Medical Imaging with Deep Learning, 2018. Abstract/PDF
  5. D. Tellez, J. van der Laak and F. Ciompi, "Gigapixel Whole-Slide Image Classification Using Unsupervised Image Compression And Contrastive Training", Medical Imaging with Deep Learning, 2018. Abstract/PDF
  6. J. Bokhorst, L. Rijstenberg, D. Goudkade, I. Nagtegaal, J. van der Laak and F. Ciompi, "Automatic Detection of Tumor Budding in Colorectal Carcinoma with Deep Learning", Computational Pathology and Ophthalmic Medical Image Analysis, 2018. Abstract/PDF DOI
  7. D. Geijs, M. Intezar, J. van der Laak and G. Litjens, "Automatic color unmixing of IHC stained whole slide images", Medical Imaging, 2018;10581. Abstract/PDF DOI

Abstracts

  1. M. Hermsen, T. de Bel, M. den Boer, E. Steenbergen, J. Kers, S. Florquin, B. Smeets, L. Hilbrands and J. van der Laak, "Glomerular detection, segmentation and counting in PAS-stained histopathological slides using deep learning", Dutch Federation of Nephrology (NfN) Fall Symposium, 2018. Abstract/PDF
  2. E. Smeets, J. Teuwen, J. van der Laak, M. Gotthardt, F. Ciompi and E. Aarntzen, "Tumor heterogeneity as a PET-biomarker predicts overall survival of pancreatic cancer patients", European Society for Molecular Imaging, 2018. Abstract

2017

Papers in international journals

  1. G. Litjens, T. Kooi, B. Bejnordi, A. Setio, F. Ciompi, M. Ghafoorian, J. van der Laak, B. van Ginneken and C. Sánchez, "A Survey on Deep Learning in Medical Image Analysis", Medical Image Analysis, 2017;42:60-88. Abstract/PDF DOI PMID arXiv
  2. A. Castells-Nobau, B. Nijhof, I. Eidhof, L. Wolf, J. Gooyert, I. Monedero, L. Torroja, J. van der Laak and A. Schenck, "Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology", JoVE, 2017;123(e55395):1-13. Abstract/PDF DOI
  3. 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/PDF DOI PMID
  4. B. 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. 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/PDF DOI PMID
  5. 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/PDF DOI PMID

Papers in conference proceedings

  1. F. Ciompi, O. Geessink, B. Bejnordi, G. de Souza, A. Baidoshvili, G. Litjens, B. van Ginneken, I. Nagtegaal and J. van der Laak, "The importance of stain normalization in colorectal tissue classification with convolutional networks", IEEE International Symposium on Biomedical Imaging, 2017:160-163. Abstract/PDF DOI arXiv
  2. P. Bándi, R. van de Loo, M. Intezar, D. Geijs, F. Ciompi, B. van Ginneken, J. van der Laak and G. Litjens, "Comparison of Different Methods for Tissue Segmentation In Histopathological Whole-Slide Images", IEEE International Symposium on Biomedical Imaging, 2017:591-595. Abstract/PDF DOI arXiv
  3. 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/PDF DOI PMID arXiv

Abstracts

  1. M. Hermsen, T. de Bel, M. van de Warenburg, J. Knuiman, E. Steenbergen, G. Litjens, B. Smeets, L. Hilbrands and J. van der Laak, "Automatic segmentation of histopathological slides from renal allograft biopsies using artificial intelligence", Dutch Federation of Nephrology (NfN) Fall Symposium, 2017. Abstract/PDF

PhD theses

  1. B. Bejnordi, "Histopathological diagnosis of breast cancer using machine learning", 2017. Abstract/PDF

2016

Papers in international journals

  1. G. Litjens, C. Sanchez, N. Timofeeva, M. Hermsen, I. Nagtegaal, I. Kovacs, C. Hulsbergen-van de Kaa, P. Bult, B. van Ginneken and J. van der Laak, "Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis", Nature Scientific Reports, 2016;6:26286. Abstract/PDF DOI PMID
  2. B. Bejnordi, G. Litjens, N. Timofeeva, I. Otte-Holler, A. Homeyer, N. Karssemeijer and J. van der Laak, "Stain specific standardization of whole-slide histopathological images", IEEE Transactions on Medical Imaging, 2016;35:404-415. Abstract/PDF DOI PMID
  3. T. Kobus, J. van der Laak, M. Maas, T. Hambrock, C. Bruggink, C. Hulsbergen-van de Kaa, T. Scheenen and A. Heerschap, "Contribution of Histopathologic Tissue Composition to Quantitative MR Spectroscopy and Diffusion-weighted Imaging of the Prostate", Radiology, 2016;278:801-811. Abstract/PDF DOI PMID
  4. B. Bejnordi, M. Balkenhol, G. Litjens, R. Holland, P. Bult, N. Karssemeijer and J. van der Laak, "Automated Detection of DCIS in Whole-Slide H&E Stained Breast Histopathology Images", IEEE Transactions on Medical Imaging, 2016;35:2141-2150. Abstract/PDF DOI PMID

Papers in conference proceedings

  1. T. Mertzanidou, J. Hipwell, S. Reis, B. Bejnordi, M. Hermsen, M. Dalmis, S. Vreemann, B. Platel, J. van der Laak, N. Karssemeijer, R. Mann, P. Bult and D. Hawkes, "Whole Mastectomy Volume Reconstruction from 2D Radiographs and Its Mapping to Histology", Breast Imaging, 2016;9699:367-374. Abstract/PDF DOI

Abstracts

  1. M. Hermsen and J. van der Laak, "Highly multiplexed immunofluorescence using spectral imaging", DPA's Pathology Visions Conference 2016, San Diego, CA, US, 2016. Abstract/PDF

2015

Papers in conference proceedings

  1. G. Litjens, B. Bejnordi, N. Timofeeva, G. Swadi, I. Kovacs, C. de Kaa and J. van der Laak, "Automated detection of prostate cancer in digitized whole-slide images of H&E-stained biopsy specimens", Medical Imaging, 2015;9420:94200B. Abstract/PDF DOI
  2. B. Bejnordi, G. Litjens, M. Hermsen, N. Karssemeijer and J. van der Laak, "A multi-scale superpixel classification approach for region of interest detection in whole slide histopathology images", Medical Imaging, 2015;9420:94200H. Abstract/PDF DOI

2014

Papers in international journals

  1. S. van der Wal, M. Vaneker, M. Steegers, V. B, M. Kox, J. van der Laak, J. van der Hoeven, K. Vissers and G. Scheffer, "Lidocaine increases the anti-inflammatory cytokine IL-10 following mechanical ventilation in healthy mice", Acta Anaesthesiologica Scandinavica, 2014;59:47-55. Abstract/PDF DOI
  2. L. Martinez, E. Friedlander, J. van der Laak and K. Hebeda, "Abundance of IgG4+ Plasma Cells in Isolated Reactive Lymphadenopathy Is No Indication of IgG4-Related Disease", American Journal of Clinical Pathology, 2014;142(4):459-466. Abstract/PDF DOI

Papers in conference proceedings

  1. B. Ehteshami Bejnordi, N. Timofeeva, I. Otte-Höller, N. Karssemeijer and J. van der Laak, "Quantitative analysis of stain variability in histology slides and an algorithm for standardization", Medical Imaging, 2014. Abstract/PDF DOI

2013

Papers in international journals

  1. R. van der Post, J. van der Laak, B. Sturm, R. Clarijs, E. Schaafsma, H. van Krieken and M. Nap, "The evaluation of colon biopsies using virtual microscopy is reliable", Histopathology, 2013;63:114-121. Abstract/PDF DOI

2012

Papers in international journals

  1. T. Roelofsen, L. van Kempen, J. van der Laak, M. van Ham, J. Bulten and L. Massuger, "Concurrent Endometrial Intraepithelial Carcinoma (EIC) and Serous Ovarian Cancer. Can EIC Be Seen as the Precursor Lesion?", International Journal of Gynaecological Cancer, 2012;22:457-464. Abstract/PDF DOI

2011

Papers in international journals

  1. M. Kox, J. Pompe, E. Peters, V. M.,, J. van der Laak, J. van der Hoeven, G. Scheffer, C. Hoedemaekers and P. Pickkers, "a7 Nicotinic acetylcholine receptor agonist GTS-21 attenuates ventilator-induced tumour necrosis factor-a production and lung injury", British Journal of Anaesthesia, 2011;107:559-566. Abstract/PDF DOI

2009

Papers in international journals

  1. C. van Niekerk, J. van der Laak, M. Börger, H. Huisman, J. Witjes, J. Barentsz and C. de Kaa, "Computerized whole slide quantification shows increased microvascular density in pT2 prostate cancer as compared to normal prostate tissue", Prostate, 2009;69:62-69. Abstract/PDF DOI PMID