Publications

all all years 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 and before most cited

2023

Papers in international journals

  1. J. Linmans, E. Hoogeboom, J. van der Laak and G. Litjens, "The Latent Doctor Model for Modeling Inter-Observer Variability", IEEE Journal of Biomedical and Health Informatics, 2023:1-12.
    Abstract DOI PMID
  2. J. Swillens, I. Nagtegaal, S. Engels, A. Lugli, R. Hermens and J. van der Laak, "Pathologists' first opinions on barriers and facilitators of computational pathology adoption in oncological pathology: an international study", Oncogene, 2023;42:2816-2827.
    Abstract DOI PMID
  3. S. Dooper, H. Pinckaers, W. Aswolinskiy, K. Hebeda, S. Jarkman, J. van der Laak and G. Litjens, "Gigapixel end-to-end training using streaming and attention", Medical Image Analysis, 2023;88:102881.
    Abstract DOI PMID Cited by ~3
  4. J. Bokhorst, I. Nagtegaal, F. Fraggetta, S. Vatrano, W. Mesker, M. Vieth, J. van der Laak and F. Ciompi, "Deep learning for multi-class semantic segmentation enables colorectal cancer detection and classification in digital pathology images", Nature Scientific Reports, 2023;13:8398.
    Abstract DOI PMID Cited by ~1
  5. A. van der Kamp, T. de Bel, L. van Alst, J. Rutgers, M. van den Heuvel-Eibrink, A. Mavinkurve-Groothuis, J. van der Laak and R. de Krijger, "Automated Deep Learning-Based Classification of Wilms Tumor Histopathology", Cancers, 2023;15:2656.
    Abstract DOI PMID
  6. B. Laarhuis, M. Janssen, M. Simons, L. van Kalmthout, M. van der Doelen, S. Peters, H. Westdorp, I. van Oort, G. Litjens, M. Gotthardt, J. Nagarajah, N. Mehra and B. Prive, "Tumoral Ki67 and PSMA Expression in Fresh Pre-PSMA-RLT Biopsies and Its Relation With PSMA-PET Imaging and Outcomes of PSMA-RLT in Patients With mCRPC.", Clinical Genitourinary Cancer, 2023.
    Abstract DOI PMID
  7. M. Schuurmans, N. Alves, P. Vendittelli, H. Huisman and J. Hermans, "Artificial Intelligence in Pancreatic Ductal Adenocarcinoma Imaging: A Commentary on Potential Future Applications.", Gastroenterology, 2023.
    Abstract DOI PMID
  8. J. Bokhorst, I. Nagtegaal, I. Zlobec, H. Dawson, K. Sheahan, F. Simmer, R. Kirsch, M. Vieth, A. Lugli, J. van der Laak and F. Ciompi, "Semi-Supervised Learning to Automate Tumor Bud Detection in Cytokeratin-Stained Whole-Slide Images of Colorectal Cancer", Cancers, 2023;15(7):2079.
    Abstract DOI PMID Cited by ~2
  9. J. Bogaerts, M. van Bommel, R. Hermens, M. Steenbeek, J. de Hullu, J. van der Laak, M. Simons and S. consortium, "Consensus based recommendations for the diagnosis of serous tubal intraepithelial carcinoma: an international Delphi study", Histopathology, 2023;83:67-79.
    Abstract DOI PMID
  10. L. van Eekelen, G. Litjens and K. Hebeda, "Artificial intelligence in bone marrow histological diagnostics: potential applications and challenges.", Pathobiology, 2023.
    Abstract DOI PMID Cited by ~2
  11. A. Baidoshvili, M. Khacheishvili, J. van der Laak and P. van Diest, "A whole-slide imaging based workflow reduces the reading time of pathologists", Pathology International, 2023;73:127-134.
    Abstract DOI PMID Cited by ~2
  12. J. Linmans, S. Elfwing, J. van der Laak and G. Litjens, "Predictive uncertainty estimation for out-of-distribution detection in digital pathology.", Medical Image Analysis, 2023;83:102655.
    Abstract DOI PMID Cited by ~16
  13. D. Page, G. Broeckx, C. Jahangir, S. Verbandt, R. Gupta, J. Thagaard, R. Khiroya, Z. Kos, K. Abduljabbar, G. Acosta Haab, B. Acs, G. Akturk, J. Almeida, I. Alvarado-Cabrero, F. Azmoudeh-Ardalan, S. Badve, N. Baharun, E. Bellolio, V. Bheemaraju, K. Blenman, L. Mendonça Botinelly Fujimoto, N. Bouchmaa, O. Burgues, M. Cheang, F. Ciompi, L. Cooper, A. Coosemans, G. Corredor, F. Dantas Portela, F. Deman, S. Demaria, S. Dudgeon, M. Elghazawy, S. Ely, C. Fernandez-Martín, S. Fineberg, S. Fox, W. Gallagher, J. Giltnane, S. Gnjatic, P. Gonzalez-Ericsson, A. Grigoriadis, N. Halama, M. Hanna, A. Harbhajanka, A. Hardas, S. Hart, J. Hartman, S. Hewitt, A. Hida, H. Horlings, Z. Husain, E. Hytopoulos, S. Irshad, E. Janssen, M. Kahila, T. Kataoka, K. Kawaguchi, D. Kharidehal, A. Khramtsov, U. Kiraz, P. Kirtani, L. Kodach, K. Korski, A. Kovács, A. Laenkholm, C. Lang-Schwarz, D. Larsimont, J. Lennerz, M. Lerousseau, X. Li, A. Ly, A. Madabhushi, S. Maley, V. Manur Narasimhamurthy, D. Marks, E. McDonald, R. Mehrotra, S. Michiels, F. Minhas, S. Mittal, D. Moore, S. Mushtaq, H. Nighat, T. Papathomas, F. Penault-Llorca, R. Perera, C. Pinard, J. Pinto-Cardenas, G. Pruneri, L. Pusztai, A. Rahman, N. Rajpoot, B. Rapoport, T. Rau, J. Reis-Filho, J. Ribeiro, D. Rimm, A. Vincent-Salomon, M. Salto-Tellez, J. Saltz, S. Sayed, K. Siziopikou, C. Sotiriou, A. Stenzinger, M. Sughayer, D. Sur, F. Symmans, S. Tanaka, T. Taxter, S. Tejpar, J. Teuwen, E. Thompson, T. Tramm, W. Tran, J. van der Laak, P. van Diest, G. Verghese, G. Viale, M. Vieth, N. Wahab, T. Walter, Y. Waumans, H. Wen, W. Yang, Y. Yuan, S. Adams, J. Bartlett, S. Loibl, C. Denkert, P. Savas, S. Loi, R. Salgado and E. Specht Stovgaard, "Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer", The Journal of Pathology, 2023;260:514-532.
    Abstract DOI Cited by ~9
  14. M. Smit, F. Ciompi, J. Bokhorst, G. van Pelt, O. Geessink, H. Putter, R. Tollenaar, J. van Krieken, W. Mesker and J. van der Laak, "Deep learning based tumor-stroma ratio scoring in colon cancer correlates with microscopic assessment", Journal of Pathology Informatics, 2023.
    Abstract DOI Cited by ~3
  15. M. Polack, M. Smit, S. Crobach, V. Terpstra, A. Roodvoets, E. Meershoek-Klein Kranenbarg, E. Dequeker, J. van der Laak, R. Tollenaar, H. van Krieken and W. Mesker, "Uniform Noting for International application of the Tumor-stroma ratio as Easy Diagnostic tool: The UNITED study - An update", European Journal of Surgical Oncology, 2023;49:e132-e133.
    Abstract DOI
  16. N. Brouwer, A. Khan, J. Bokhorst, F. Ayatollahi, J. Hay, F. Ciompi, F. Simmer, N. Hugen, J. de Wilt, M. Berger, A. Lugli, I. Zlobec, J. Edwards and I. Nagtegaal, "The complexity of shapes; how the circularity of tumor nodules impacts prognosis in colorectal cancer", Modern Pathology, 2023:100376.
    Abstract DOI
  17. W. Aswolinskiy, E. Munari, H. Horlings, L. Mulder, G. Bogina, J. Sanders, Y. Liu, A. van den Belt-Dusebout, L. Tessier, M. Balkenhol, M. Stegeman, J. Hoven, J. Wesseling, J. van der Laak, E. Lips and F. Ciompi, "PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning", Breast Cancer Research, 2023;25.
    Abstract DOI
  18. L. Menotti, G. Silvello, M. Atzori, S. Boytcheva, F. Ciompi, G. Di Nunzio, F. Fraggetta, F. Giachelle, O. Irrera, S. Marchesin, N. Marini, H. Müller and T. Primov, "Modelling digital health data: The ExaMode ontology for computational pathology", Journal of Pathology Informatics, 2023;14:100332.
    Abstract DOI
  19. J. Bokhorst, F. Ciompi, S. Öztürk, A. Oguz Erdogan, M. Vieth, H. Dawson, R. Kirsch, F. Simmer, K. Sheahan, A. Lugli, I. Zlobec, J. van der Laak and I. Nagtegaal, "Fully Automated Tumor Bud Assessment in Hematoxylin and Eosin-Stained Whole Slide Images of Colorectal Cancer", Modern Pathology, 2023;36:100233.
    Abstract DOI
  20. P. Bándi, M. Balkenhol, M. van Dijk, M. Kok, B. van Ginneken, J. van der Laak and G. Litjens, "Continual learning strategies for cancer-independent detection of lymph node metastases", Medical Image Analysis, 2023;85:102755.
    Abstract DOI Cited by ~4
  21. J. Thagaard, G. Broeckx, D. Page, C. Jahangir, S. Verbandt, Z. Kos, R. Gupta, R. Khiroya, K. Abduljabbar, G. Acosta Haab, B. Acs, G. Akturk, J. Almeida, I. Alvarado-Cabrero, M. Amgad, F. Azmoudeh-Ardalan, S. Badve, N. Baharun, E. Balslev, E. Bellolio, V. Bheemaraju, K. Blenman, L. Mendonça Botinelly Fujimoto, N. Bouchmaa, O. Burgues, A. Chardas, M. U Chon Cheang, F. Ciompi, L. Cooper, A. Coosemans, G. Corredor, A. Dahl, F. Dantas Portela, F. Deman, S. Demaria, J. Doré Hansen, S. Dudgeon, T. Ebstrup, M. Elghazawy, C. Fernandez-Martín, S. Fox, W. Gallagher, J. Giltnane, S. Gnjatic, P. Gonzalez-Ericsson, A. Grigoriadis, N. Halama, M. Hanna, A. Harbhajanka, S. Hart, J. Hartman, S. Hauberg, S. Hewitt, A. Hida, H. Horlings, Z. Husain, E. Hytopoulos, S. Irshad, E. Janssen, M. Kahila, T. Kataoka, K. Kawaguchi, D. Kharidehal, A. Khramtsov, U. Kiraz, P. Kirtani, L. Kodach, K. Korski, A. Kovács, A. Laenkholm, C. Lang-Schwarz, D. Larsimont, J. Lennerz, M. Lerousseau, X. Li, A. Ly, A. Madabhushi, S. Maley, V. Manur Narasimhamurthy, D. Marks, E. McDonald, R. Mehrotra, S. Michiels, F. Minhas, S. Mittal, D. Moore, S. Mushtaq, H. Nighat, T. Papathomas, F. Penault-Llorca, R. Perera, C. Pinard, J. Pinto-Cardenas, G. Pruneri, L. Pusztai, A. Rahman, N. Rajpoot, B. Rapoport, T. Rau, J. Reis-Filho, J. Ribeiro, D. Rimm, A. Roslind, A. Vincent-Salomon, M. Salto-Tellez, J. Saltz, S. Sayed, E. Scott, K. Siziopikou, C. Sotiriou, A. Stenzinger, M. Sughayer, D. Sur, S. Fineberg, F. Symmans, S. Tanaka, T. Taxter, S. Tejpar, J. Teuwen, E. Thompson, T. Tramm, W. Tran, J. van der Laak, P. van Diest, G. Verghese, G. Viale, M. Vieth, N. Wahab, T. Walter, Y. Waumans, H. Wen, W. Yang, Y. Yuan, R. Zin, S. Adams, J. Bartlett, S. Loibl, C. Denkert, P. Savas, S. Loi, R. Salgado and E. Specht Stovgaard, "Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer", The Journal of Pathology, 2023;260:498-513.
    Abstract DOI Cited by ~3

Preprints

  1. Y. Jiao, J. van der Laak, S. Albarqouni, Z. Li, T. Tan, A. Bhalerao, J. Ma, J. Sun, J. Pocock, J. Pluim, N. Koohbanani, R. Bashir, S. Raza, S. Liu, S. Graham, S. Wetstein, S. Khurram, T. Watson, N. Rajpoot, M. Veta and F. Ciompi, "LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset", arXiv:2301.06304, 2023.
    Abstract DOI arXiv Cited by ~1
  2. A. Reinke, M. Tizabi, M. Baumgartner, M. Eisenmann, D. Heckmann-Notzel, A. Kavur, T. Radsch, C. Sudre, L. Acion, M. Antonelli, T. Arbel, S. Bakas, A. Benis, M. Blaschko, F. Buttner, M. Cardoso, V. Cheplygina, J. Chen, E. Christodoulou, B. Cimini, G. Collins, K. Farahani, L. Ferrer, A. Galdran, B. van Ginneken, B. Glocker, P. Godau, R. Haase, D. Hashimoto, M. Hoffman, M. Huisman, F. Isensee, P. Jannin, C. Kahn, D. Kainmueller, B. Kainz, A. Karargyris, A. Karthikesalingam, H. Kenngott, J. Kleesiek, F. Kofler, T. Kooi, A. Kopp-Schneider, M. Kozubek, A. Kreshuk, T. Kurc, B. Landman, G. Litjens, A. Madani, K. Maier-Hein, A. Martel, P. Mattson, E. Meijering, B. Menze, K. Moons, H. Muller, B. Nichyporuk, F. Nickel, J. Petersen, S. Rafelski, N. Rajpoot, M. Reyes, M. Riegler, N. Rieke, J. Saez-Rodriguez, C. Sanchez, S. Shetty, M. van Smeden, R. Summers, A. Taha, A. Tiulpin, S. Tsaftaris, B. Van Calster, G. Varoquaux, M. Wiesenfarth, Z. Yaniv, P. Jager and L. Maier-Hein, "Understanding metric-related pitfalls in image analysis validation", arXiv:2302.01790, 2023.
    Abstract arXiv Cited by ~10

Papers in conference proceedings

  1. D. Schouten and G. Litjens, "PythoStitcher: an iterative approach for stitching digitized tissue fragments into full resolution whole-mount reconstructions", Medical Imaging, 2023;12471:1247118.
    Abstract DOI
  2. J. Spronck, T. Gelton, L. van Eekelen, J. Bogaerts, L. Tessier, M. van Rijthoven, L. van der Woude, M. van den Heuvel, W. Theelen, J. van der Laak and F. Ciompi, "nnUNet meets pathology: bridging the gap for application to whole-slide images and computational biomarkers", Medical Imaging with Deep Learning, 2023.
    Abstract Url
  3. P. Vendittelli, J. Bokhorst, E. Smeets, V. Kryklyva, L. Brosens, C. Verbeke and G. Litjens, "Automatic quantification of TSR as a prognostic marker for pancreatic cancer.", Medical Imaging with Deep Learning, 2023.
    Abstract Url

Abstracts

  1. R. Lomans, R. van der Post and F. Ciompi, "Interactive Cell Detection in H&E-stained slides of Diffuse Gastric Cancer", Medical Imaging with Deep Learning, 2023.
    Abstract
  2. M. D'Amato, M. Balkenhol, M. van Rijthoven, J. van der Laak and F. Ciompi, "On the robustness of regressing tumor percentage as an explainable detector in histopathology whole-slide images", Medical Imaging with Deep Learning, 2023.
    Abstract
  3. R. Leon-Ferre, J. Carter, D. Zahrieh, J. Sinnwell, R. Salgado, V. Suman, D. Hillman, J. Boughey, K. Kalari, F. Couch, J. Ingle, M. Balkenkohl, F. Ciompi, J. van der Laak and M. Goetz, "Abstract P2-11-34: Mitotic spindle hotspot counting using deep learning networks is highly associated with clinical outcomes in patients with early-stage triple-negative breast cancer who did not receive systemic therapy", Cancer Research, 2023;83:P2-11-34-P2-11-34.
    Abstract DOI
  4. R. Lomans, J. van der Laak, I. Nagtegaal, F. Ciompi and R. van der Post, "Deep learning for multi-class cell detection in H&E-stained slides of diffuse gastric cancer", European Congress of Pathology, 2023.
    Abstract
  5. B. Guevara, N. Marini, S. Marchesin, W. Aswolinskiy, R. Schlimbach, D. Podareanu and F. Ciompi, "Caption generation from histopathology whole-slide images using pre-trained transformers", Medical Imaging with Deep Learning, 2023.
    Abstract

Other publications

  1. M. Aubreville, N. Stathonikos, C. Bertram, R. Klopfleisch, N. Hoeve, F. Ciompi, F. Wilm, C. Marzahl, T. Donovan, A. Maier, M. Veta and K. Breininger, "Abstract: the MIDOG Challenge 2021", Bildverarbeitung fur die Medizin, Workshop, 2023:115-115.
    Abstract DOI