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.
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.
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.
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.
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.
A. Baidoshvili, N. Stathonikos, G. Freling, J. Bart, N. 't Hart, J. van der Laak, J. Doff, B. van der Vegt, M. Kluin Philip and P. van Dies, "Validation of a whole-slide image-based teleconsultation network",
Histopathology,
2018;73:777-783.
G. Litjens, P. Bándi, B. Ehteshami 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(6):1-8.
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.
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.
F. Zanjani, S. Zinger, B. Bejnordi, J. van der Laak and P. de With, "Stain normalization of histopathology images using generative adversarial networks",
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018),
2018.
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.
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.
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.
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.
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.
T. de Bel, M. Hermsen, J. van der Laak, G. Litjens, B. Smeets and L. Hilbrands, "Automatic segmentation of histopathological slides of renal tissue using deep learning",
Medical Imaging 2018: Digital Pathology,
2018.
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.
F. Zanjani, S. Zinger, B. Bejnordi, J. van der Laak and P. de With, "Histopathology stain-color normalization using deep generative models",
Medical Imaging with Deep Learning,
2018.
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.
T. de Bel, M. Hermsen, G. Litjens and J. van der Laak, "Structure Instance Segmentation in Renal Tissue: A Case Study on Tubular Immune Cell Detection",
Computational Pathology and Ophthalmic Medical Image Analysis,
2018:112-119.