Publications of Jeroen van der Laak

2021

Papers in international journals

1. M. Yousif, P. van Diest, A. Laurinavicius, D. Rimm, J. van der Laak, A. Madabhushi, S. Schnitt and L. Pantanowitz, "Artificial intelligence applied to breast pathology", Virchows Archiv, 2021;480:191-209.
   Abstract DOI PMID Cited by ~32
2. J. Rutgers, T. Bánki, A. van der Kamp, T. Waterlander, M. Scheijde-Vermeulen, M. van den Heuvel-Eibrink, J. van der Laak, M. Fiocco, A. Mavinkurve-Groothuis and R. de Krijger, "Interobserver variability between experienced and inexperienced observers in the histopathological analysis of Wilms tumors: a pilot study for future algorithmic approach", Diagnostic Pathology, 2021;16.
   Abstract DOI PMID Cited by ~3
3. J. Slaats, C. Dieteren, E. Wagena, L. Wolf, T. Raaijmakers, J. van der Laak, C. Figdor, B. Weigelin and P. Friedl, "Metabolic Screening of Cytotoxic T-cell Effector Function Reveals the Role of CRAC Channels in Regulating Lethal Hit Delivery", Cancer Immunology Research, 2021;9:926-938.
   Abstract DOI PMID Cited by ~4
4. M. Hermsen, V. Volk, J. Brasen, D. Geijs, W. Gwinner, J. Kers, J. Linmans, N. Schaadt, J. Schmitz, E. Steenbergen, Z. Swiderska-Chadaj, B. Smeets, L. Hilbrands and J. van der Laak, "Quantitative assessment of inflammatory infiltrates in kidney transplant biopsies using multiplex tyramide signal amplification and deep learning", Laboratory Investigation, 2021;101(8):970-982.
   Abstract DOI PMID Download Cited by ~27
5. J. van der Laak, G. Litjens and F. Ciompi, "Deep learning in histopathology: the path to the clinic.", Nature Medicine, 2021;27(5):775-784.
   Abstract DOI PMID Download Cited by ~368
6. T. Haddad, A. Lugli, S. Aherne, V. Barresi, B. Terris, J. Bokhorst, S. Brockmoeller, M. Cuatrecasas, F. Simmer, H. El-Zimaity, J. Fléjou, D. Gibbons, G. Cathomas, R. Kirsch, T. Kuhlmann, C. Langner, M. Loughrey, R. Riddell, A. Ristimäki, S. Kakar, K. Sheahan, D. Treanor, J. van der Laak, M. Vieth, I. Zlobec and I. Nagtegaal, "Improving tumor budding reporting in colorectal cancer: a Delphi consensus study", Virchows Archiv, 2021;479:459-469.
   Abstract DOI PMID Cited by ~27
7. T. de Bel, J. Bokhorst, J. van der Laak and G. Litjens, "Residual cyclegan for robust domain transformation of histopathological tissue slides.", Medical Image Analysis, 2021;70:102004.
   Abstract DOI PMID Download Cited by ~61
8. M. Balkenhol, F. Ciompi, Z. Swiderska-Chadaj, R. van de Loo, M. Intezar, I. Otte-Holler, D. Geijs, J. Lotz, N. Weiss, T. de Bel, G. Litjens, P. Bult and J. van der Laak, "Optimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognostics.", The Breast, 2021;56:78-87.
   Abstract DOI PMID Cited by ~20
9. M. van Rijthoven, M. Balkenhol, K. Silina, J. van der Laak and F. Ciompi, "HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images", Medical Image Analysis, 2021;68:101890.
   Abstract DOI PMID Algorithm Download Cited by ~104
10. 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, 2021;43(2):567-578.
    Abstract DOI PMID Download Cited by ~168
11. F. Ciompi, M. Veta, J. van der Laak and N. Rajpoot, "Editorial Computational Pathology", IEEE} Journal of Biomedical and Health Informatics, 2021;25(2):303-306.
    Abstract DOI
12. N. Marini, S. Otálora, D. Podareanu, M. van Rijthoven, J. van der Laak, F. Ciompi, H. Muller and M. Atzori, "Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images", Frontiers in Computer Science, 2021;3.
    Abstract DOI Cited by ~15
13. J. Bogaerts, M. Steenbeek, M. van Bommel, J. Bulten, J. van der Laak, J. de Hullu and M. Simons, "Recommendations for diagnosing STIC: a systematic review and meta-analysis", 2021;480(4):725-737.
    Abstract DOI Download Cited by ~13

Preprints

1. J. Bokhorst, I. Nagtegaal, F. Fraggetta, S. Vatrano, W. Mesker, M. Vieth, J. van der Laak and F. Ciompi, "Automated risk classification of colon biopsies based on semantic segmentation of histopathology images", arXiv:2109.07892, 2021.
   Abstract DOI arXiv Cited by ~1
2. J. Lotz, N. Weiss, J. van der Laak and S. Heldmann, "Comparison of Consecutive and Re-stained Sections for Image Registration in Histopathology", arXiv:2106.13150, 2021.
   Abstract DOI arXiv Cited by ~6

Papers in conference proceedings

1. K. Faryna, J. van der Laak and G. Litjens, "Tailoring automated data augmentation to H&E-stained histopathology", Medical Imaging with Deep Learning, 2021.
   Abstract Url Cited by ~31
2. G. Smit, F. Ciompi, M. Cigéhn, A. Bodén, J. van der Laak and C. Mercan, "Quality control of whole-slide images through multi-class semantic segmentation of artifacts", Medical Imaging with Deep Learning, 2021.
   Abstract Url Cited by ~10
3. M. van Rijthoven, M. Balkenhol, M. Atzori, P. Bult, J. van der Laak and F. Ciompi, "Few-shot weakly supervised detection and retrieval in histopathology whole-slide images", Medical Imaging, 2021;11603:137 - 143.
   Abstract DOI Cited by ~1
4. W. Aswolinskiy, D. Tellez, G. Raya, L. van der Woude, M. Looijen-Salamon, J. van der Laak, K. Grunberg and F. Ciompi, "Neural image compression for non-small cell lung cancer subtype classification in H&E stained whole-slide images", Medical Imaging 2021: Digital Pathology, 2021;11603:1 - 7.
   Abstract DOI Cited by ~8

PhD theses

1. D. Tellez, "Advancing computational pathology with deep learning: from patches to gigapixel image-level classification", PhD thesis, 2021.
   Abstract Url