Publications

Papers in international journals

1. 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.
   Abstract DOI
2. 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
3. M. Aubreville, N. Stathonikos, C. Bertram, R. Klopfleisch, N. Ter Hoeve, F. Ciompi, F. Wilm, C. Marzahl, T. Donovan, A. Maier, J. Breen, N. Ravikumar, Y. Chung, J. Park, R. Nateghi, F. Pourakpour, R. Fick, S. Ben Hadj, M. Jahanifar, A. Shephard, J. Dexl, T. Wittenberg, S. Kondo, M. Lafarge, V. Koelzer, J. Liang, Y. Wang, X. Long, J. Liu, S. Razavi, A. Khademi, S. Yang, X. Wang, R. Erber, A. Klang, K. Lipnik, P. Bolfa, M. Dark, G. Wasinger, M. Veta and K. Breininger, "Mitosis domain generalization in histopathology images - The MIDOG challenge.", Medical Image Analysis, 2022;84:102699.
   Abstract DOI PMID
4. L. Adams, M. Makowski, G. Engel, M. Rattunde, F. Busch, P. Asbach, S. Niehues, S. Vinayahalingam, B. van Ginneken, G. Litjens and K. Bressem, "Dataset of prostate MRI annotated for anatomical zones and cancer.", Data in brief, 2022;45:108739.
   Abstract DOI PMID
5. C. Mercan, M. Balkenhol, R. Salgado, M. Sherman, P. Vielh, W. Vreuls, A. Polónia, H. Horlings, W. Weichert, J. Carter, P. Bult, M. Christgen, C. Denkert, K. van de Vijver, J. Bokhorst, J. van der Laak and F. Ciompi, "Deep learning for fully-automated nuclear pleomorphism scoring in breast cancer.", NPJ breast cancer, 2022;8(1):120.
   Abstract DOI PMID
6. S. Marchesin, F. Giachelle, N. Marini, M. Atzori, S. Boytcheva, G. Buttafuoco, F. Ciompi, G. Di Nunzio, F. Fraggetta, O. Irrera, H. Müller, T. Primov, S. Vatrano and G. Silvello, "Empowering digital pathology applications through explainable knowledge extraction tools.", Journal of pathology informatics, 2022;13:100139.
   Abstract DOI PMID
7. E. Munari, G. Querzoli, M. Brunelli, M. Marconi, M. Sommaggio, M. Cocchi, G. Martignoni, G. Netto, A. Caliò, L. Quatrini, F. Mariotti, C. Luchini, I. Girolami, A. Eccher, D. Segala, F. Ciompi, G. Zamboni, L. Moretta and G. Bogina, "Comparison of three validated PD-L1 immunohistochemical assays in urothelial carcinoma of the bladder: interchangeability and issues related to patient selection.", Frontiers in immunology, 2022;13:954910.
   Abstract DOI PMID
8. N. Marini, S. Marchesin, S. Otálora, M. Wodzinski, A. Caputo, M. van Rijthoven, W. Aswolinskiy, J. Bokhorst, D. Podareanu, E. Petters, S. Boytcheva, G. Buttafuoco, S. Vatrano, F. Fraggetta, J. van der Laak, M. Agosti, F. Ciompi, G. Silvello, H. Muller and M. Atzori, "Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations.", NPJ digital medicine, 2022;5(1):102.
   Abstract DOI PMID
9. M. Hermsen, F. Ciompi, A. Adefidipe, A. Denic, A. Dendooven, B. Smith, D. van Midden, J. Brasen, J. Kers, M. Stegall, P. Bandi, T. Nguyen, Z. Swiderska-Chadaj, B. Smeets, L. Hilbrands and J. van der Laak, "Convolutional neural networks for the evaluation of chronic and inflammatory lesions in kidney transplant biopsies", American Journal of Pathology, 2022;192(10):1418-1432.
   Abstract DOI PMID
10. L. Adams, M. Makowski, G. Engel, M. Rattunde, F. Busch, P. Asbach, S. Niehues, S. Vinayahalingam, B. van Ginneken, G. Litjens and K. Bressem, "Prostate158 - An expert-annotated 3T MRI dataset and algorithm for prostate cancer detection.", Computers in biology and medicine, 2022;148:105817.
    Abstract DOI PMID
11. M. Antonelli, A. Reinke, S. Bakas, K. Farahani, A. Kopp-Schneider, B. Landman, G. Litjens, B. Menze, O. Ronneberger, R. Summers, B. van Ginneken, M. Bilello, P. Bilic, P. Christ, R. Do, M. Gollub, S. Heckers, H. Huisman, W. Jarnagin, M. McHugo, S. Napel, J. Pernicka, K. Rhode, C. Tobon-Gomez, E. Vorontsov, J. Meakin, S. Ourselin, M. Wiesenfarth, P. Arbelaez, B. Bae, S. Chen, L. Daza, J. Feng, B. He, F. Isensee, Y. Ji, F. Jia, I. Kim, K. Maier-Hein, D. Merhof, A. Pai, B. Park, M. Perslev, R. Rezaiifar, O. Rippel, I. Sarasua, W. Shen, J. Son, C. Wachinger, L. Wang, Y. Wang, Y. Xia, D. Xu, Z. Xu, Y. Zheng, A. Simpson, L. Maier-Hein and M. Cardoso, "The Medical Segmentation Decathlon", Nature Communications, 2022;13(1):4128.
    Abstract DOI PMID
12. G. Litjens, F. Ciompi and J. van der Laak, "A Decade of GigaScience: The Challenges of Gigapixel Pathology Images.", GigaScience, 2022;11.
    Abstract DOI PMID Download
13. H. Pinckaers, J. van Ipenburg, J. Melamed, A. De Marzo, E. Platz, B. van Ginneken, J. van der Laak and G. Litjens, "Predicting biochemical recurrence of prostate cancer with artificial intelligence", Communications Medicine, 2022;2:64.
    Abstract DOI PMID
14. I. Girolami, L. Pantanowitz, S. Marletta, M. Hermsen, J. van der Laak, E. Munari, L. Furian, F. Vistoli, G. Zaza, M. Cardillo, L. Gesualdo, G. Gambaro and A. Eccher, "Artificial intelligence applications for pre-implantation kidney biopsy pathology practice: a systematic review.", Journal of nephrology, 2022.
    Abstract DOI PMID
15. S. Satturwar, I. Girolami, E. Munari, F. Ciompi, A. Eccher and L. Pantanowitz, "Program death ligand-1 immunocytochemistry in lung cancer cytological samples: A systematic review.", Diagnostic cytopathology, 2022;50(6):313-323.
    Abstract DOI PMID
16. W. Bulten, K. Kartasalo, P. Chen, P. Strom, H. Pinckaers, K. Nagpal, Y. Cai, D. Steiner, H. van Boven, R. Vink, C. de Hulsbergen-van Kaa, J. van der Laak, M. Amin, A. Evans, T. van der Kwast, R. Allan, P. Humphrey, H. Gronberg, H. Samaratunga, B. Delahunt, T. Tsuzuki, T. Hakkinen, L. Egevad, M. Demkin, S. Dane, F. Tan, M. Valkonen, G. Corrado, L. Peng, C. Mermel, P. Ruusuvuori, G. Litjens, M. Eklund, A. Brilhante, A. Cakir, X. Farre, K. Geronatsiou, V. Molinie, G. Pereira, P. Roy, G. Saile, P. Salles, E. Schaafsma, J. Tschui, J. Billoch-Lima, E. Pereira, M. Zhou, S. He, S. Song, Q. Sun, H. Yoshihara, T. Yamaguchi, K. Ono, T. Shen, J. Ji, A. Roussel, K. Zhou, T. Chai, N. Weng, D. Grechka, M. Shugaev, R. Kiminya, V. Kovalev, D. Voynov, V. Malyshev, E. Lapo, M. Campos, N. Ota, S. Yamaoka, Y. Fujimoto, K. Yoshioka, J. Juvonen, M. Tukiainen, A. Karlsson, R. Guo, C. Hsieh, I. Zubarev, H. Bukhar, W. Li, J. Li, W. Speier, C. Arnold, K. Kim, B. Bae, Y. Kim, H. Lee, J. Park and the PANDA challenge consortium, "Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge", Nature Medicine, 2022.
    Abstract DOI PMID
17. M. D'Amato, P. Szostak and B. Torben-Nielsen, "A Comparison Between Single- and Multi-Scale Approaches for Classification of Histopathology Images", Frontiers in Public Health, 2022;10.
    Abstract DOI
18. M. Schuurmans, N. Alvás, P. Vendittelli, H. Huisman and J. Hermans, "Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging", Cancers, 2022:3498.
    Abstract DOI
19. L. van Eekelen, H. Pinckaers, M. van den Brand, K. Hebeda and G. Litjens, "Using deep learning for quantification of cellularity and cell lineages in bone marrow biopsies and comparison to normal age-related variation.", Pathology, 2021.
    Abstract DOI PMID Download
20. K. Kartasalo, W. Bulten, B. Delahunt, P. Chen, H. Pinckaers, H. Olsson, X. Ji, N. Mulliqi, H. Samaratunga, T. Tsuzuki, J. Lindberg, M. Rantalainen, C. Wahlby, G. Litjens, P. Ruusuvuori, L. Egevad and M. Eklund, "Artificial Intelligence for Diagnosis and Gleason Grading of Prostate Cancer in Biopsies-Current Status and Next Steps.", European Urology Focus, 2021;7(4):687-691.
    Abstract DOI PMID Download
21. E. Munari, M. Marconi, G. Querzoli, G. Lunardi, P. Bertoglio, F. Ciompi, A. Tosadori, A. Eccher, N. Tumino, L. Quatrini, P. Vacca, G. Rossi, A. Cavazza, G. Martignoni, M. Brunelli, G. Netto, L. Moretta, G. Zamboni and G. Bogina, "Impact of PD-L1 and PD-1 Expression on the Prognostic Significance of CD8+, Tumor-Infiltrating Lymphocytes in Non-Small Cell Lung Cancer.", Frontiers in immunology, 2021;12:680973.
    Abstract DOI PMID Download
22. E. Munari, F. Mariotti, L. Quatrini, P. Bertoglio, N. Tumino, P. Vacca, A. Eccher, F. Ciompi, M. Brunelli, G. Martignoni, G. Bogina and L. Moretta, "PD-1/PD-L1 in Cancer: Pathophysiological, Diagnostic and Therapeutic Aspects.", International journal of molecular sciences, 2021;22(10).
    Abstract DOI PMID
23. 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
24. 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
25. F. Faita, T. Oranges, N. Di Lascio, F. Ciompi, S. Vitali, G. Aringhieri, A. Janowska, M. Romanelli and V. Dini, "Ultra-high-frequency ultrasound and machine learning approaches for the differential diagnosis of melanocytic lesions.", Experimental Dermatology, 2021.
    Abstract DOI PMID Download
26. H. Pinckaers, W. Bulten, J. der Van Laak and G. Litjens, "Detection of prostate cancer in whole-slide images through end-to-end training with image-level labels.", IEEE Transactions on Medical Imaging, 2021.
    Abstract DOI PMID Download
27. 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
28. 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
29. O. Turner, B. Knight, A. Zuraw, G. Litjens and D. Rudmann, "Mini Review: The Last Mile-Opportunities and Challenges for Machine Learning in Digital Toxicologic Pathology.", Toxicologic Pathology, 2021;49(4):714-719.
    Abstract DOI PMID Download
30. 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
31. N. Lessmann, C. Sánchez, L. Beenen, L. Boulogne, M. Brink, E. Calli, J. Charbonnier, T. Dofferhoff, W. van Everdingen, P. Gerke, B. Geurts, H. Gietema, M. Groeneveld, L. van Harten, N. Hendrix, W. Hendrix, H. Huisman, I. Isgum, C. Jacobs, R. Kluge, M. Kok, J. Krdzalic, B. Lassen-Schmidt, K. van Leeuwen, J. Meakin, M. Overkamp, T. van Rees Vellinga, E. van Rikxoort, R. Samperna, C. Schaefer-Prokop, S. Schalekamp, E. Scholten, C. Sital, L. Stöger, J. Teuwen, K. Vaidhya Venkadesh, C. de Vente, M. Vermaat, W. Xie, B. de Wilde, M. Prokop and B. van Ginneken, "Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence", Radiology, 2021;298(1):E18-E28.
    Abstract DOI PMID Algorithm Download
32. 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
33. 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
34. 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
35. 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
36. 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 DOI PMID
37. Z. Swiderska-Chadaj, K. Hebeda, M. van den Brand and G. Litjens, "Artificial intelligence to detect MYC translocation in slides of diffuse large B-cell lymphoma", Virchows Archiv, 2020.
    Abstract DOI PMID
38. Z. Swiderska-Chadaj, T. de Bel, L. Blanchet, A. Baidoshvili, D. Vossen, J. van der Laak and G. Litjens, "Impact of rescanning and normalization on convolutional neural network performance in multi-center, whole-slide classification of prostate cancer", Scientific Reports, 2020;10(1):14398.
    Abstract DOI PMID Download
39. H. Pinckaers, B. van Ginneken and G. Litjens, "Streaming convolutional neural networks for end-to-end learning with multi-megapixel images", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.
    Abstract DOI PMID arXiv
40. 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 Hulsbergen-van Kaa and G. Litjens, "Artificial Intelligence Assistance Significantly Improves Gleason Grading of Prostate Biopsies by Pathologists", Modern Pathology, 2020.
    Abstract DOI PMID
41. G. van Leenders, T. van der Kwast, D. Grignon, A. Evans, G. Kristiansen, C. Kweldam, G. Litjens, J. McKenney, J. Melamed, N. Mottet, G. Paner, H. Samaratunga, I. Schoots, J. Simko, T. Tsuzuki, M. Varma, A. Warren, T. Wheeler, S. Williamson, K. Iczkowski and I. Members, "The 2019 International Society of Urological Pathology (ISUP) Consensus Conference on Grading of Prostatic Carcinoma.", American Journal of Surgical Pathology, 2020;44(8):e87-e99.
    Abstract DOI PMID
42. 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(1).
    Abstract DOI PMID Cited by ~1 Download
43. 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(1).
    Abstract DOI PMID Cited by ~2 Download
44. M. Balkenhol, W. Vreuls, C. Wauters, S. Mol, J. van der Laak and P. Bult, "Histological subtypes in triple negative breast cancer are associated with specific information on survival", Annals of Diagnostic Pathology, 2020;46:151490.
    Abstract DOI PMID Download
45. W. Bulten, H. Pinckaers, H. van Boven, R. Vink, T. de Bel, B. van Ginneken, J. van der Laak, C. de Hulsbergen-van 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 DOI PMID arXiv Cited by ~29 Algorithm Download
46. F. Ayatollahi, S. Shokouhi and J. Teuwen, "Differentiating Benign and Malignant Mass and non-Mass Lesions in Breast DCE-MRI using Normalized Frequency-based Features", International Journal of Computer Assisted Radiology and Surgery, 2020;15(2):297-307.
    Abstract DOI PMID
47. 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 ~2
48. 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 ~2
49. O. Debats, G. Litjens and H. Huisman, "Lymph node detection in MR Lymphography: false positive reduction using multi-view convolutional neural networks", PeerJ, 2019;7:e8052.
    Abstract DOI PMID Download
50. 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 Cited by ~1 Download
51. 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 Cited by ~2 Download
52. 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 ~25
53. 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 ~9
54. 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 DOI PMID Cited by ~28
55. 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 Cited by ~2 Download
56. G. Litjens, F. Ciompi, J. Wolterink, B. de Vos, T. Leiner, J. Teuwen and I. Isgum, "State-of-the-Art Deep Learning in Cardiovascular Image Analysis", JACC Cardiovascular Imaging, 2019;12(8 Pt 1):1549-1565.
    Abstract DOI PMID Cited by ~34 Download
57. 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 Cited by ~24 Download
58. 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 ~7
59. N. Khalili, N. Lessmann, E. Turk, N. Claessens, R. de Heus, T. Kolk, M. Viergever, M. Benders and I. Išgum, "Automatic brain tissue segmentation in fetal MRI using convolutional neural networks", Magnetic Resonance Imaging, 2019;64:77-89.
    Abstract DOI PMID Cited by ~13
60. 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 ~6
61. 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 Cited by ~3 Download
62. 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 Cited by ~8 Download
63. 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 ~1
64. M. Maas, G. Litjens, A. Wright, U. Attenberger, M. Haider, T. Helbich, B. Kiefer, K. Macura, D. Margolis, A. Padhani, K. Selnaes, G. Villeirs, J. Futterer and T. Scheenen, "A Single-Arm, Multicenter Validation Study of Prostate Cancer Localization and Aggressiveness With a Quantitative Multiparametric Magnetic Resonance Imaging Approach", Investigative Radiology, 2019.
    Abstract DOI PMID Cited by ~2
65. M. Veta, Y. Heng, N. Stathonikos, B. Bejnordi, F. Beca, T. Wollmann, K. Rohr, M. Shah, D. Wang, M. Rousson, M. Hedlund, D. Tellez, F. Ciompi, E. Zerhouni, D. Lanyi, M. Viana, V. Kovalev, V. Liauchuk, H. Phoulady, T. Qaiser, S. Graham, N. Rajpoot, E. Sjoblom, J. Molin, K. Paeng, S. Hwang, S. Park, Z. Jia, E. Chang, Y. Xu, A. Beck, P. van Diest and J. Pluim, "Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge", Medical Image Analysis, 2019;54(5):111-121.
    Abstract DOI PMID Cited by ~56
66. 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 Cited by ~8 Download
67. 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 DOI PMID arXiv Cited by ~39
68. 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 DOI PMID Cited by ~54
69. 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 DOI PMID
70. S. Balocco, F. Ciompi, J. Rigla, X. Carrillo, J. Mauri and P. Radeva, "Assessment Of Intra-coronary Stent Location And Extension In Intravascular Ultrasound Sequences", Medical Physics, 2018;46(2):484-493.
    Abstract DOI PMID Cited by ~1
71. M. Silva, M. Prokop, C. Jacobs, G. Capretti, N. Sverzellati, F. Ciompi, B. van Ginneken, C. Schaefer-Prokop, C. Galeone, A. Marchiano and U. Pastorino, "Long-term Active Surveillance of Screening Detected Subsolid Nodules is a Safe Strategy to Reduce Overtreatment", Journal of Thoracic Oncology, 2018;13:1454-1463.
    Abstract DOI PMID Cited by ~15 Download
72. 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.
    Abstract DOI PMID Cited by ~45
73. 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 DOI PMID Cited by ~21 Download
74. 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.
    Abstract DOI PMID Cited by ~52
75. 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.
    Abstract DOI PMID Cited by ~6
76. G. Litjens, P. Bandi, 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.
    Abstract DOI PMID Cited by ~48
77. 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 DOI PMID Cited by ~1
78. M. Silva, C. Schaefer-Prokop, C. Jacobs, G. Capretti, F. Ciompi, B. van Ginneken, U. Pastorino and N. Sverzellati, "Detection of Subsolid Nodules in Lung Cancer Screening: Complementary Sensitivity of Visual Reading and Computer-Aided Diagnosis", Investigative Radiology, 2018;53(8):441-449.
    Abstract DOI PMID Cited by ~13 Download
79. K. Chung, F. Ciompi, J. Scholten E. Th. Goo, M. Prokop, C. Jacobs, B. van Ginneken and C. Schaefer-Prokop, "Visual Discrimination of Screen-detected Persistent from Transient Subsolid Nodules: an Observer Study", PLoS One, 2018;13(2):e0191874.
    Abstract DOI PMID Cited by ~4 Download
80. J. Charbonnier, K. Chung, E. Scholten, E. van Rikxoort, C. Jacobs, N. Sverzellati, M. Silva, U. Pastorino, B. van Ginneken and F. Ciompi, "Automatic segmentation of the solid core and enclosed vessels in subsolid pulmonary nodules", Nature Scientific Reports, 2018;8(1):646.
    Abstract DOI PMID Cited by ~9 Download
81. 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 DOI PMID Cited by ~75 Download
82. B. Ehteshami 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. Ahmady 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 DOI PMID Cited by ~791
83. S. van Riel, F. Ciompi, M. Winkler Wille, A. Dirksen, S. Lam, E. Scholten, S. Rossi, N. Sverzellati, M. Naqibullah, R. Wittenberg, M. Hovinga-de Boer, M. Snoeren, L. Peters-Bax, O. Mets, M. Brink, M. Prokop, C. Schaefer-Prokop and B. van Ginneken, "Malignancy risk estimation of pulmonary nodules in screening CTs: Comparison between a computer model and human observers", PLoS One, 2017;12(11):e0185032.
    Abstract DOI PMID Cited by ~18
84. F. Ciompi, K. Chung, S. van Riel, A. Setio, P. Gerke, C. Jacobs, E. Scholten, C. Schaefer-Prokop, M. Wille, A. Marchiano, U. Pastorino, M. Prokop and B. van Ginneken, "Towards automatic pulmonary nodule management in lung cancer screening with deep learning", Nature Scientific Reports, 2017(46479).
    Abstract DOI PMID arXiv Cited by ~171 Download
85. G. Litjens, T. Kooi, B. Ehteshami 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 DOI PMID arXiv Cited by ~3681 Download
86. A. Setio, A. Traverso, T. de Bel, M. Berens, C. Bogaard, P. Cerello, H. Chen, Q. Dou, M. Fantacci, B. Geurts, R. Gugten, P. Heng, B. Jansen, M. de Kaste, V. Kotov, J. Lin, J. Manders, A. Sonora-Mengana, J. Garcia-Naranjo, E. Papavasileiou, M. Prokop, M. Saletta, C. Schaefer-Prokop, E. Scholten, L. Scholten, M. Snoeren, E. Torres, J. Vandemeulebroucke, N. Walasek, G. Zuidhof, B. Ginneken and C. Jacobs, "Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge", Medical Image Analysis, 2017;42:1-13.
    Abstract DOI PMID arXiv Cited by ~291 Download
87. M. Ghafoorian, N. Karssemeijer, T. Heskes, I. van Uden, C. Sánchez, G. Litjens, F. de Leeuw, B. van Ginneken, E. Marchiori and B. Platel, "Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities", Nature Scientific Reports, 2017;7(1):5110.
    Abstract DOI PMID arXiv Cited by ~117
88. J. van Zelst, M. Balkenhol, T. Tan, M. Rutten, M. Imhof-Tas, P. Bult, N. Karssemeijer and R. Mann, "Sonographic Phenotypes of Molecular Subtypes of Invasive Ductal Cancer in Automated 3-D Breast Ultrasound", Ultrasound in Medicine and Biology, 2017;43(9):1820-1828.
    Abstract DOI PMID Cited by ~9
89. A. Castells-Nobau, B. Nijhof, I. Eidhof, L. Wolf, J. Scheffer-de 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 DOI PMID Cited by ~5
90. K. Chung, C. Jacobs, E. Scholten, J. Goo, H. Prosch, N. Sverzellati, F. Ciompi, O. Mets, P. Gerke, M. Prokop, B. van Ginneken and C. Schaefer-Prokop, "Lung-RADS Category 4X: Does It Improve Prediction of Malignancy in Subsolid Nodules?", Radiology, 2017;284(1):264-271.
    Abstract DOI PMID Cited by ~32
91. S. van Riel, F. Ciompi, C. Jacobs, M. Winkler Wille, E. Scholten, M. Naqibullah, S. Lam, M. Prokop, C. Schaefer-Prokop and B. van Ginneken, "Malignancy risk estimation of screen-detected nodules at baseline CT: comparison of the PanCan model, Lung-RADS and NCCN guidelines", European Radiology, 2017;27(10):4019-4029.
    Abstract DOI PMID Cited by ~28 Download
92. S. Laban, G. Giebel, N. Klümper, A. Schröck, J. Doescher, G. Spagnoli, J. Thierauf, M. Theodoraki, R. Remark, S. Gnjatic, R. Krupar, A. Sikora, G. Litjens, N. Grabe, G. Kristiansen, F. Bootz, P. Schuler, C. Brunner, J. Brägelmann, T. Hoffmann and S. Perner, "MAGE expression in head and neck squamous cell carcinoma primary tumors, lymph node metastases and respective recurrences: implications for immunotherapy", Oncotarget, 2017;8:14719-14735.
    Abstract DOI PMID Cited by ~17
93. S. Steens, E. Bekers, W. Weijs, G. Litjens, A. Veltien, A. Maat, G. van den Broek, J. van der Laak, J. Futterer, C. van der Kaa, M. Merkx and R. Takes, "Evaluation of tongue squamous cell carcinoma resection margins using ex-vivo MR.", International Journal of Computer Assisted Radiology and Surgery, 2017;12(5):821-828.
    Abstract DOI PMID Download
94. 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 DOI PMID Cited by ~11 Download
95. M. Dalmis, G. Litjens, K. Holland, A. Setio, R. Mann, N. Karssemeijer and A. Gubern-Mérida, "Using deep learning to segment breast and fibroglandular tissue in MRI volumes", Medical Physics, 2017;44(2):533-546.
    Abstract DOI PMID Cited by ~113 Download
96. J. Charbonnier, E. van Rikxoort, A. Setio, C. Schaefer-Prokop, B. van Ginneken and F. Ciompi, "Improving Airway Segmentation in Computed Tomography using Leak Detection with Convolutional Networks", Medical Image Analysis, 2017;36:52-60.
    Abstract DOI PMID Cited by ~49
97. T. Kooi, G. Litjens, B. van Ginneken, A. Gubern-Mérida, C. Sánchez, R. Mann, A. den Heeten and N. Karssemeijer, "Large scale deep learning for computer aided detection of mammographic lesions", Medical Image Analysis, 2017;35:303-312.
    Abstract DOI PMID Cited by ~432 Download
98. R. Remark, T. Merghoub, N. Grabe, G. Litjens, D. Damotte, J. Wolchok, M. Merad and S. Gnjatic, "In-depth tissue profiling using multiplexed immunohistochemical consecutive staining on single slide", Science Immunology, 2016;1(1):aaf6925-aaf6925.
    Abstract DOI PMID Cited by ~63
99. F. Ciompi, S. Balocco, J. Rigla, X. Carrillo, J. Mauri and P. Radeva, "Computer-aided detection of intracoronary stent in intravascular ultrasound sequences", Medical Physics, 2016;43(10):5616.
    Abstract DOI PMID Cited by ~7
100. O. Debats, A. Fortuin, H. Meijer, T. Hambrock, G. Litjens, J. Barentsz and H. Huisman, "Intranodal signal suppression in pelvic MR lymphography of prostate cancer patients: a quantitative comparison of ferumoxtran-10 and ferumoxytol", PeerJ, 2016;4:e2471.
     Abstract DOI PMID Cited by ~7 Download
101. O. Debats, M. Meijs, G. Litjens and H. Huisman, "Automated multistructure atlas-assisted detection of lymph nodes using pelvic MR lymphography in prostate cancer patients", Medical Physics, 2016;43(6):3132.
     Abstract DOI PMID Cited by ~2 Download
102. G. Litjens, C. Sánchez, 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 DOI PMID Cited by ~490
103. 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(9):2141-2150.
     Abstract DOI PMID Cited by ~55
104. A. Setio, F. Ciompi, G. Litjens, P. Gerke, C. Jacobs, S. van Riel, M. Wille, M. Naqibullah, C. Sánchez and B. van Ginneken, "Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks", IEEE Transactions on Medical Imaging, 2016;35(5):1160-1169.
     Abstract DOI PMID Cited by ~605 Download
105. J. Charbonnier, M. Brink, F. Ciompi, E. Scholten, C. Schaefer-Prokop and E. van Rikxoort, "Automatic Pulmonary Artery-Vein Separation and Classification in Computed Tomography Using Tree Partitioning and Peripheral Vessel Matching", IEEE Transactions on Medical Imaging, 2016:882-892.
     Abstract DOI PMID Cited by ~21
106. 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(3):801-811.
     Abstract DOI PMID Cited by ~26 Download
107. 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(2):404-415.
     Abstract DOI PMID Cited by ~128
108. G. Litjens, R. Elliott, N. Shih, M. Feldman, T. Kobus, C. Hulsbergen-van de Kaa, J. Barentsz, H. Huisman and A. Madabhushi, "Computer-extracted Features Can Distinguish Noncancerous Confounding Disease from Prostatic Adenocarcinoma at Multiparametric MR Imaging.", Radiology, 2016;278(1):135-145.
     Abstract DOI PMID
109. F. Ciompi, B. de Hoop, S. van Riel, K. Chung, E. Scholten, M. Oudkerk, P. de Jong, M. Prokop and B. van Ginneken, "Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box", Medical Image Analysis, 2015;26(1):195-202.
     Abstract DOI PMID Cited by ~191 Download
110. G. Litjens, J. Barentsz, N. Karssemeijer and H. Huisman, "Clinical evaluation of a computer-aided diagnosis system for determining cancer aggressiveness in prostate MRI", European Radiology, 2015;25(11):3187-3199.
     Abstract DOI PMID Cited by ~47 Download
111. E. Vos, T. Kobus, G. Litjens, T. Hambrock, C. de Hulsbergen-van Kaa, J. Barentsz, M. Maas and T. Scheenen, "Multiparametric Magnetic Resonance Imaging for Discriminating Low-Grade From High-Grade Prostate Cancer", Investigative Radiology, 2015;50:490-497.
     Abstract DOI PMID Download
112. F. Ciompi, C. Jacobs, E. Scholten, M. Winkler Wille, P. de Jong, M. Prokop and B. van Ginneken, "Bag of frequencies: a descriptor of pulmonary nodules in Computed Tomography images", IEEE Transactions on Medical Imaging, 2015;34(4):1-12.
     Abstract DOI PMID Cited by ~41
113. C. Gatta and F. Ciompi, "Stacked sequential scale-space Taylor context", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014;36(8):1694-1700.
     Abstract DOI PMID Cited by ~11 Download
114. G. Litjens, H. Huisman, R. Elliott, N. Shih, M. Feldman, S. Viswanath, J. Fütterer, J. Bomers and A. Madabhushi, "Quantitative identification of magnetic resonance imaging features of prostate cancer response following laser ablation and radical prostatectomy", Journal of Medical Imaging, 2014;1(3):035001-035001.
     Abstract DOI PMID Cited by ~16
115. G. Litjens, O. Debats, J. Barentsz, N. Karssemeijer and H. Huisman, "Computer-aided detection of prostate cancer in MRI", IEEE Transactions on Medical Imaging, 2014;33(5):1083-1092.
     Abstract DOI PMID Cited by ~234 Download
116. G. Litjens, R. Toth, W. van de Ven, C. Hoeks, S. Kerkstra, B. van Ginneken, G. Vincent, G. Guillard, N. Birbeck, J. Zhang, R. Strand, F. Malmberg, Y. Ou, C. Davatzikos, M. Kirschner, F. Jung, J. Yuan, W. Qiu, Q. Gao, P. Edwards, B. Maan, F. van der Heijden, S. Ghose, J. Mitra, J. Dowling, D. Barratt, H. Huisman and A. Madabhushi, "Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge", Medical Image Analysis, 2014;18(2):359-373.
     Abstract DOI PMID Cited by ~275 Download
117. S. Balocco, C. Gatta, F. Ciompi, A. Wahle, P. Radeva, S. Carlier, G. Unal, E. Sanidas, J. Mauri, X. Carillo, T. Kovarnik, C. Wang, H. Chen, T. Exarchos, D. Fotiadis, F. Destrempes, G. Cloutier, O. Pujol, M. Alberti, E. Mendizabal-Ruiz, M. Rivera, T. Aksoy, R. Downe and I. Kakadiaris, "Standardized evaluation methodology and reference database for evaluating IVUS image segmentation", Computerized Medical Imaging and Graphics, 2014;38:70-90.
     Abstract DOI PMID Cited by ~88
118. F. Ciompi, O. Pujol and P. Radeva, "ECOC-DRF: Discriminative Random Fields based on Error-Correcting Output Codes", Pattern Recognition, 2014;47:2193-2204.
     Abstract DOI Cited by ~5 Download
119. L. Louzao 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 DOI Cited by ~20
120. 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 DOI Cited by ~22
121. E. Vos, G. Litjens, T. Kobus, T. Hambrock, C. Kaa, J. Barentsz, H. Huisman and T. Scheenen, "Assessment of Prostate Cancer Aggressiveness Using Dynamic Contrast-enhanced Magnetic Resonance Imaging at 3T", European Urology, 2013;64:448-455.
     Abstract PMID Url Cited by ~150 Download
122. K. Nagel, M. Schouten, T. Hambrock, G. Litjens, C. Hoeks, B. Haken, J. Barentsz and J. Fütterer, "Differentiation of Prostatitis and Prostate Cancer by Using Diffusion-weighted MR Imaging and MR-guided Biopsy at 3 T", RADIOLOGY, 2013;267:164-172.
     Abstract DOI PMID Download
123. 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 DOI Cited by ~21
124. G. Litjens, T. Hambrock, C. de Hulsbergen-van Kaa, J. Barentsz and H. Huisman, "Interpatient Variation in Normal Peripheral Zone Apparent Diffusion Coefficient: Effect on the Prediction of Prostate Cancer Aggressiveness", Radiology, 2012;265(1):260-266.
     Abstract DOI PMID Cited by ~60 Download
125. F. Ciompi, O. Pujol, C. Gatta, M. Alberti, S. Balocco, X. Carrillo, J. Mauri-Ferre and P. Radeva, "HoliMAb: A holistic approach for Media--Adventitia border detection in intravascular ultrasound", Medical Image Analysis, 2012.
     Abstract PMID Url Cited by ~53
126. M. Alberti, S. Balocco, C. Gatta, F. Ciompi, O. Pujol, J. Silva, X. Carrillo and P. Radeva, "Automatic bifurcation detection in coronary IVUS sequences", IEEE Transactions on Biomedical Engineering, 2012;59(4):1022-1031.
     Abstract DOI PMID Cited by ~42
127. 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(3):457-464.
     Abstract DOI Cited by ~27
128. O. Debats, G. Litjens, J. Barentsz, N. Karssemeijer and H. Huisman, "Automated 3-Dimensional Segmentation of Pelvic Lymph Nodes in Magnetic Resonance Images", Medical Physics, 2011;38(11):6178-6187.
     Abstract DOI PMID Cited by ~12 Download
129. X. Carrillo, E. Fernandez-Nofrerias, F. Ciompi, O. Rodriguez-Leor, P. Radeva, N. Salvatella, O. Pujol, J. Mauri and A. Bayes-Genis, "Changes in radial artery volume assessed using intravascular ultrasound: a comparison of two vasodilator regimens in transradial coronary interventions", Journal of Invasive Cardiology, 2011;23(10):401-404.
     Abstract PMID Url Cited by ~20
130. 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(4):559-566.
     Abstract DOI Download
131. J. Seabra, F. Ciompi, O. Pujol, J. Mauri, P. Radeva and J. Sanches, "Rayleigh mixture model for plaque characterization in intravascular ultrasound", IEEE Transactions on Biomedical Engineering, 2011;58(5):1314-1324.
     Abstract Url Cited by ~80
132. F. Ciompi, O. Pujol, C. Gatta, O. Rodriguez-Leor, J. Mauri-Ferre and P. Radeva, "Fusing in-vitro and in-vivo intravascular ultrasound data for plaque characterization", International Journal of Cardiac Imaging, 2010;26(7):763-779.
     Abstract PMID Url Cited by ~28
133. C. van Niekerk, J. van der Laak, M. Börger, H. Huisman, J. Witjes, J. Barentsz and C. de Hulsbergen-van Kaa, "Computerized whole slide quantification shows increased microvascular density in pT2 prostate cancer as compared to normal prostate tissue", Prostate, 2009;69(1):62-69.
     Abstract DOI PMID Cited by ~40 Download
134. J. van der Laak, M. Pahlplatz, A. Hanselaar and P. de Wilde, "Hue-saturation-density (HSD) model for stain recognition in digital images from transmitted light microscopy", Cytometry, 2000;39(4):275-284.
     Abstract PMID Download

Preprints

1. A. Reinke, M. Eisenmann, M. Tizabi, C. Sudre, T. Radsch, M. Antonelli, T. Arbel, S. Bakas, M. Cardoso, V. Cheplygina, K. Farahani, B. Glocker, D. Heckmann-Notzel, F. Isensee, P. Jannin, C. Kahn, J. Kleesiek, T. Kurc, M. Kozubek, B. Landman, G. Litjens, K. Maier-Hein, B. Menze, H. Muller, J. Petersen, M. Reyes, N. Rieke, B. Stieltjes, R. Summers, S. Tsaftaris, B. van Ginneken, A. Kopp-Schneider, P. Jager and L. Maier-Hein, "Common Limitations of Image Processing Metrics: A Picture Story", arXiv preprint arXiv:2104.05642, 2021.
   Abstract arXiv
2. M. Antonelli, A. Reinke, S. Bakas, K. Farahani, AnnetteKopp-Schneider, B. Landman, G. Litjens, B. Menze, O. Ronneberger, R. Summers, B. van Ginneken, M. Bilello, P. Bilic, P. Christ, R. Do, M. Gollub, S. Heckers, H. Huisman, W. Jarnagin, M. McHugo, S. Napel, J. Pernicka, K. Rhode, C. Tobon-Gomez, E. Vorontsov, H. Huisman, J. Meakin, S. Ourselin, M. Wiesenfarth, P. Arbelaez, B. Bae, S. Chen, L. Daza, J. Feng, B. He, F. Isensee, Y. Ji, F. Jia, N. Kim, I. Kim, D. Merhof, A. Pai, B. Park, M. Perslev, R. Rezaiifar, O. Rippel, I. Sarasua, W. Shen, J. Son, C. Wachinger, L. Wang, Y. Wang, Y. Xia, D. Xu, Z. Xu, Y. Zheng, A. Simpson, L. Maier-Hein and M. Cardoso, "The Medical Segmentation Decathlon", arXiv preprint arXiv:2106.05735, 2021.
   Abstract arXiv
3. A. Simpson, M. Antonelli, S. Bakas, M. Bilello, K. Farahani, B. van Ginneken, A. Kopp-Schneider, B. Landman, G. Litjens, B. Menze, O. Ronneberger, R. Summers, P. Bilic, P. Christ, R. Do, M. Gollub, J. Golia-Pernicka, S. Heckers, W. Jarnagin, M. McHugo, S. Napel, E. Vorontsov, L. Maier-Hein and M. Cardoso, "A large annotated medical image dataset for the development and evaluation of segmentation algorithms", arXiv:1902.09063, 2019.
   Abstract arXiv Cited by ~75
4. G. Mooij, I. Bagulho and H. Huisman, "Automatic segmentation of prostate zones", arXiv:1806.07146, 2018.
   Abstract arXiv Cited by ~10

Papers in conference proceedings

1. E. Chelebian and F. Ciompi, "Seeded iterative clustering for histology region identification", Learning Meaningful Representations of Life, NeurIPS 2022, 2022.
   Abstract
2. L. Studer, J. Bokhorst, F. Ciompi, A. Fischer and H. Dawson, "Building-T-cell score is a potential predictor for more aggressive treatment in pT1 colorectal cancers", Proceedings of the ECDP 2022 18th European Congress on Digital Pathology, 2022.
   Abstract
3. A. Saha, J. Bosma, J. Linmans, M. Hosseinzadeh and H. Huisman, "Anatomical and Diagnostic Bayesian Segmentation in Prostate MRI -- Should Different Clinical Objectives Mandate Different Loss Functions?", Medical Imaging Meets NeurIPS Workshop - 35th Conference on Neural Information Processing Systems (NeurIPS), 2021.
   Abstract arXiv
4. 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
5. A. Reinke, M. Eisenmann, M. Tizabi, C. Sudre, T. Radsch, M. Antonelli, T. Arbel, S. Bakas, J. Cardoso, V. Cheplygina, K. Farahani, B. Glocker, D. Heckmann-Notzel, F. Isensee, P. Jannin, C. Kahn, J. Kleesiek, T. Kurc, M. Kozubek, B. Landman, G. Litjens, K. Maier-Hein, A. Martel, H. Muller, J. Petersen, M. Reyes, N. Rieke, B. Stieltjes, R. Summers, S. Tsaftaris, B. van Ginneken, A. Kopp-Schneider, P. Jager and L. Maier-Hein, "Common limitations of performance metrics in biomedical image analysis", Medical Imaging with Deep Learning, 2021.
   Abstract DOI
6. N. Marini, S. Otalora, F. Ciompi, G. Silvello, S. Marchesin, S. Vatrano, G. Buttafuoco, M. Atzori, H. Muller, N. Burlutskiy, Z. Li, F. Minhas, T. Peng, N. Rajpoot, B. Torbennielsen, J. Der Van Laak, M. Veta, Y. Yuan and I. Zlobec, "Multi-Scale Task Multiple Instance Learning for the Classification of Digital Pathology Images with Global Annotations", 2021.
   Abstract
7. D. Geijs, H. Pinckaers, A. Amir and G. Litjens, "End-to-end classification on basal-cell carcinoma histopathology whole-slides images", Medical Imaging, 2021;11603:1160307.
   Abstract DOI
8. J. Vermazeren, L. van Eekelen, L. Meesters, M. Looijen-Salamon, S. Vos, E. Munari, C. Mercan and F. Ciompi, "muPEN: Multi-class PseudoEdgeNet for PD-L1 assessment", Medical Imaging with Deep Learning, 2021.
   Abstract Url
9. 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
10. 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
11. 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
12. R. Fick, B. Tayart, C. Bertrand, S. Lang, T. Rey, F. Ciompi, C. Tilmant, I. Farre and S. Hadj, "A Partial Label-Based Machine Learning Approach For Cervical Whole-Slide Image Classification: The Winning TissueNet Solution", 2021 43rd Annual International Conference of the {IEEE} Engineering in Medicine and Biology Society ({EMBC}), 2021.
    Abstract DOI
13. K. Faryna, F. Tushar, V. D'Anniballe, R. Hou, G. Rubin and J. Lo, "Attention-guided classification of abnormalities in semi-structured computed tomography reports", Medical Imaging, 2020;11314:397 - 403.
    Abstract DOI
14. A. Saha, F. Tushar, K. Faryna, V. D'Anniballe, R. Hou, M. Mazurowski, G. Rubin and J. Lo, "Weakly Supervised 3D Classification of Chest CT using Aggregated Multi-Resolution Deep Segmentation Features", Medical Imaging, 2020;11314:39 - 44.
    Abstract DOI arXiv
15. J. Linmans, J. van der Laak and G. Litjens, "Efficient Out-of-Distribution Detection in Digital Pathology Using Multi-Head Convolutional Neural Networks", Medical Imaging with Deep Learning, 2020:465-478.
    Abstract Url
16. 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
17. Z. Swiderska-Chadaj, K. Hebeda, M. van den Brand and G. Litjens, "Predicting MYC translocation in HE specimens of diffuse large B-cell lymphoma through deep learning", Medical Imaging, 2020;11320:1132010.
    Abstract DOI
18. Z. Swiderska-Chadaj, K. Nurzynska, G. Bartlomiej, K. Grunberg, L. van der Woude, M. Looijen-Salamon, A. Walts, T. Markiewicz, F. Ciompi and A. Gertych, "A deep learning approach to assess the predominant tumor growth pattern in whole-slide images of lung adenocarcinoma", Medical Imaging, 2020;11320:113200D.
    Abstract DOI
19. Z. Swiderska-Chadaj, E. Stoelinga, A. Gertych and F. Ciompi, "Multi-Patch Blending improves lung cancer growth pattern segmentation in whole-slide images", IEEE International Conference on Computational Problems of Electrical Engineering, 2020.
    Abstract DOI
20. L. van Eekelen, H. Pinckaers, K. Hebeda and G. Litjens, "Multi-class semantic cell segmentation and classification of aplasia in bone marrow histology images", Medical Imaging, 2020;11320:113200B.
    Abstract DOI
21. K. Faryna, K. Koschmieder, M. Paul, T. van den Heuvel, A. van der Eerden, R. Manniesing and B. van Ginneken, "Adversarial cycle-consistent synthesis of cerebral microbleeds for data augmentation", Medical Imaging Meets NeurIPS Workshop - 34th Conference on Neural Information Processing Systems (NeurIPS), 2020.
    Abstract arXiv
22. C. Mercan, G. Reijnen-Mooij, D. Martin, J. Lotz, N. Weiss, M. van Gerven and F. Ciompi, "Virtual staining for mitosis detection in Breast Histopathology", IEEE International Symposium on Biomedical Imaging, 2020:1770-1774.
    Abstract DOI
23. K. Dercksen, W. Bulten and G. Litjens, "Dealing with Label Scarcity in Computational Pathology: A Use Case in Prostate Cancer Classification", Medical Imaging with Deep Learning, 2019.
    Abstract Url Cited by ~2
24. 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 ~2
25. 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
26. H. Pinckaers, W. Bulten and G. Litjens, "High resolution whole prostate biopsy classification using streaming stochastic gradient descent", Medical Imaging, 2019(1).
    Abstract DOI Cited by ~1
27. 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 ~9
28. 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 DOI Cited by ~3
29. W. Bulten and G. Litjens, "Unsupervised Prostate Cancer Detection on H&E using Convolutional Adversarial Autoencoders", Medical Imaging with Deep Learning, 2018.
    Abstract Url Cited by ~8
30. 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 Url Cited by ~1
31. 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 Url Cited by ~5
32. 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 DOI Cited by ~12
33. 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 DOI
34. 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 DOI Cited by ~2
35. 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 Url Cited by ~2
36. 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 DOI PMID arXiv Cited by ~38
37. 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 DOI arXiv Cited by ~14
38. 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 DOI arXiv Cited by ~75
39. N. Lessmann, I. Išgum, A. Setio, B. de Vos, F. Ciompi, P. de Jong, M. Oudkerk, W. Mali, M. Viergever and B. van Ginneken, "Deep convolutional neural networks for automatic coronary calcium scoring in a screening study with low-dose chest CT", Medical Imaging, 2016;9785:978511-1 - 978511-6.
    Abstract DOI Cited by ~25
40. 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 DOI Cited by ~3
41. G. Litjens, K. Safferling and N. Grabe, "Automated robust registration of grossly misregistered whole-slide images with varying stains", Medical Imaging, 2016;9791:979103.
    Abstract DOI
42. B. van Ginneken, A. Setio, C. Jacobs and F. Ciompi, "Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans", IEEE International Symposium on Biomedical Imaging, 2015:286-289.
    Abstract DOI Cited by ~227
43. S. van de Leemput, F. Dorssers and B. Ehteshami Bejnordi, "A novel spherical shell filter for reducing false positives in automatic detection of pulmonary nodules in thoracic CT scans", Medical Imaging, 2015;9414:94142P.
    Abstract DOI
44. G. Litjens, B. Bejnordi, N. Timofeeva, G. Swadi, I. Kovacs, C. de Hulsbergen-van 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 DOI
45. A. Setio, C. Jacobs, F. Ciompi, S. van Riel, M. Wille, A. Dirksen, E. van Rikxoort and B. van Ginneken, "Computer-aided detection of lung cancer: combining pulmonary nodule detection systems with a tumor risk prediction model", Medical Imaging, 2015;9414(94141O).
    Abstract DOI Cited by ~2
46. F. Ciompi, C. Jacobs, E. Scholten, S. van Riel, M. Wille, M. Prokop and B. van Ginneken, "Automatic detection of spiculation of pulmonary nodules in Computed Tomography images", Medical Imaging, 2015;9414(941409).
    Abstract DOI Cited by ~6
47. B. Bejnordi, G. Litjens, M. Hermsen, N. Karssemeijer and J. van der Laak, "A multi-scale superpixel classification approach to the detection of regions of interest in whole slide histopathology images", Medical Imaging, 2015;9420:94200H.
    Abstract DOI Download
48. Q. Mahmood, A. Chodorowski, B. Ehteshami Bejnordi and M. Persson, "A fully automatic unsupervised segmentation framework for the brain tissues in MR images", Medical Imaging, 2014.
    Abstract DOI
49. G. Litjens, R. Elliott, N. Shih, M. Feldman, J. Barentsz, C. - van de Hulsbergen Kaa, I. Kovacs, H. Huisman and A. Madabhushi, "Distinguishing prostate cancer from benign confounders via a cascaded classifier on multi-parametric MRI", Medical Imaging, 2014;9035:903512.
    Abstract DOI Cited by ~18
50. G. Litjens, H. Huisman, R. Elliott, N. Shih, M. Feldman, Fütterer, J. Bomers and A. Madabhushi, "Distinguishing benign confounding treatment changes from residual prostate cancer on MRI following laser ablation", Medical Imaging, 2014;9036:90361D.
    Abstract DOI Cited by ~3
51. 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 DOI Cited by ~20 Download
52. F. Ciompi, R. Hua, S. Balocco, M. Alberti, O. Pujol, C. Caus, J. Mauri and P. Radeva, "Learning to Detect Stent Struts in Intravascular Ultrasound", Pattern Recognition and Image Analysis, 2013:575-583.
    Abstract Url Cited by ~4
53. F. Ciompi, S. Balocco, C. Caus, J. Mauri and P. Radeva, "Stent Shape Estimation through a Comprehensive Interpretation of Intravascular Ultrasound Images", Medical Image Computing and Computer-Assisted Intervention, 2013:345-352.
    Abstract Cited by ~6
54. B. Ehteshami Bejnordi, R. Moshavegh, K. Sujathan, P. Malm, E. Bengtsson and A. Mehnert, "Novel chromatin texture features for the classification of pap smears", Medical Imaging, 2013.
    Abstract DOI
55. G. Litjens, O. Debats, W. van de Ven, N. Karssemeijer and H. Huisman, "A pattern recognition approach to zonal segmentation of the prostate on MRI", Medical Image Computing and Computer-Assisted Intervention, 2012;7511:413-420.
    Abstract DOI Cited by ~58 Download
56. G. Litjens, N. Karssemeijer and H. Huisman, "A multi-atlas approach for prostate segmentation in MRI", MICCAI} {W}orkshop: {P}rostate {C}ancer {I}maging: The {PROMISE12} Prostate Segmentation Challenge, 2012.
    Abstract
57. G. Litjens, J. Barentsz, N. Karssemeijer and H. Huisman, "Automated computer-aided detection of prostate cancer in MR images: from a whole-organ to a zone-based approach", Medical Imaging, 2012;8315(1):83150G-83150G-6.
    Abstract DOI Cited by ~24
58. R. Moshavegh, B. Ehteshami Bejnordi, A. Mehnert, K. Sujathan, P. Malm and E. Bengtsson, "Automated segmentation of free-lying cell nuclei in Pap smears for malignancy-associated change analysis", Engineering in Medicine and Biology Society (EMBC), 2012.
    Abstract DOI
59. W. van de Ven, G. Litjens, J. Barentsz, T. Hambrock and H. Huisman, "Required accuracy of MR-US registration for prostate biopsies", P}rostate {C}ancer {I}maging. {I}mage {A}nalysis and {I}mage-{G}uided {I}nterventions, 2011;6963:92-99.
    Abstract DOI Cited by ~8
60. F. Ciompi, O. Pujol, C. Gatta, X. Carrillo, J. Mauri and P. Radeva, "A holistic approach for the detection of media-adventitia border in IVUS", Medical Image Computing and Computer-Assisted Intervention, 2011:411-419.
    Abstract Url Cited by ~11
61. S. Balocco, C. Gatta, F. Ciompi, O. Pujol, X. Carrillo, J. Mauri and P. Radeva, "Combining Growcut and temporal correlation for IVUS lumen segmentation", Pattern Recognition and Image Analysis, 2011:556-563.
    Abstract Url Cited by ~25
62. M. Alberti, C. Gatta, S. Balocco, F. Ciompi, O. Pujol, J. Silva, X. Carrillo and P. Radeva, "Automatic branching detection in IVUS sequences", Pattern Recognition and Image Analysis, 2011:126-133.
    Abstract Url Cited by ~4
63. G. Litjens, P. Vos, J. Barentsz, N. Karssemeijer and H. Huisman, "Automatic Computer Aided Detection of Abnormalities in Multi-Parametric Prostate MRI", Medical Imaging, 2011;7963(1).
    Abstract DOI Cited by ~42
64. G. Litjens, L. Hogeweg, A. Schilham, P. de Jong, M. Viergever and B. van Ginneken, "Simulation of nodules and diffuse infiltrates in chest radiographs using CT templates", Medical Image Computing and Computer-Assisted Intervention, 2010;6362:396-403.
    Abstract DOI PMID Cited by ~3
65. G. Litjens, M. Heisen, J. Buurman and B. ter Romeny, "Pharmacokinetic models in clinical practice: what model to use for DCE-MRI of the breast?", IEEE International Symposium on Biomedical Imaging, 2010:185-188.
    Abstract DOI Cited by ~11
66. F. Ciompi, O. Pujol and P. Radeva, "A meta-learning approach to conditional random fields using error-correcting output codes", International Conference on Pattern Recognition, 2010:710-713.
    Abstract Url Cited by ~5
67. H. Huisman, P. Vos, G. Litjens, T. Hambrock and J. Barentsz, "Computer aided detection of prostate cancer using t2w, DWI and DCE-MRI: methods and clinical applications", MICCAI} {W}orkshop: {P}rostate {C}ancer {I}maging: {C}omputer {A}ided {D}iagnosis, {P}rognosis, and {I}ntervention, 2010.
    Abstract
68. P. Snoeren, G. Litjens, B. van Ginneken and N. Karssemeijer, "Training a Computer Aided Detection System with Simulated Lung Nodules in Chest Radiographs", The Third International Workshop on Pulmonary Image Analysis, 2010:139-149.
    Abstract Cited by ~9
69. J. Seabra, J. Sanches, F. Ciompi and P. Radeva, "Ultrasonographic plaque characterization using a rayleigh mixture model", IEEE International Symposium on Biomedical Imaging, 2010:1-4.
    Abstract Url Cited by ~11
70. C. Gatta, S. Balocco, F. Ciompi, R. Hemetsberger, O. Leor and P. Radeva, "Real-time gating of IVUS sequences based on motion blur analysis: method and quantitative validation", Medical Image Computing and Computer-Assisted Intervention, 2010:59-67.
    Abstract DOI Cited by ~27
71. C. Gatta, J. Valencia, F. Ciompi, O. Leor and P. Radeva, "Toward robust myocardial blush grade estimation in contrast angiography", Pattern Recognition and Image Analysis, 2009:249-256.
    Abstract Url Cited by ~2
72. F. Ciompi, O. Pujol, O. Leor, C. Gatta, A. Vida and P. Radeva, "Enhancing in-vitro IVUS data for tissue characterization", Pattern Recognition and Image Analysis, 2009:241-248.
    Abstract Url Cited by ~6
73. F. Ciompi, O. Pujol, E. Fernandez-Nofrerias, J. Mauri and P. Radeva, "Ecoc random fields for lumen segmentation in radial artery ivus sequences", Medical Image Computing and Computer-Assisted Intervention, 2009:869-876.
    Abstract Url Cited by ~24

Abstracts

1. L. van Eekelen, E. Munari, I. Girolami, A. Eccher, J. van der Laak, K. Grünberg, M. Looijen-Salamon, S. Vos and F. Ciompi, "Inter-rater agreement of pathologists on determining PD-L1 status in non-small cell lung cancer", European Congress of Pathology, 2022.
   Abstract
2. L. van Eekelen, E. Munari, L. Meesters, G. de Souza, M. Demirel-Andishmand, D. Zegers, M. Looijen-Salamon, S. Vos and F. Ciompi, "Nuclei detection with YOLOv5 in PD-L1 stained non-small cell lung cancer whole slide images", European Congress of Pathology, 2022.
   Abstract
3. J. Spronck, L. Eekelen, L. Tessier, J. Bogaerts, L. van der Woude, M. van den Heuvel, W. Theelen and F. Ciompi, "Deep learning-based quantification of immune infiltrate for predicting response to pembrolizumab from pre-treatment biopsies of metastatic non-small cell lung cancer: A study on the PEMBRO-RT phase II trial", Immuno-Oncology and Technology, 2022.
   Abstract
4. E. van Genugten, B. Piet, G. Schreibelt, T. van Oorschot, G. van den Heuvel, F. Ciompi, C. Jacobs, J. de Vries, M. van den Heuvel and E. Aarntzen, "Imaging tumor-infiltrating CD8 (+) T-cells in non-small cell lung cancer patients upon neo-adjuvant treatment with durvalumab", European Molecular Imaging Meeting, 2022.
   Abstract
5. Y. Jiao, M. Rijthoven, J. Li, K. Grunberg, S. Fei and F. Ciompi, "Automatic Lung Cancer Segmentation in Histopathology Whole-Slide Images with Deep Learning", European Congress on Digital Pathology (ECDP), 2021.
   Abstract
6. 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
7. L. Studer, J. Bokhorst, I. Zlobec, A. Lugli, A. Fischer, F. Ciompi, J. van der Laak, I. Nagtegaal and H. Dawson, "Validation of computer-assisted tumour-bud and T-cell detection in pT1 colorectal cancer", European Congress of pathology, 2020.
   Abstract
8. M. Balkenhol, P. Bult, D. Tellez, W. Vreuls, P. Clahsen, F. Ciompi and J. der Laak, "Deep learning enables fully automated mitotic density assessment in breast cancer histopathology", European Journal of Cancer, 2020.
   Abstract
9. 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
10. T. Haddad, J. Bokhorst, L. van den Dobbelsteen, F. Simmer, J. van der Laak and I. Nagtegaal, "Characterisation of the tumour-host interface as a prognostic factor through deep learning systems", United European Gastroenterology Journal, 2020.
    Abstract
11. C. Mercan, M. Balkenhol, J. Laak and F. Ciompi, "Grading nuclear pleomorphism in breast cancer using deep learning", European Congress of Pathology, 2020.
    Abstract
12. H. van Zeeland, J. Meakin, B. Liefers, C. González-Gonzalo, A. Vaidyanathan, B. van Ginneken, C. Klaver and C. Sánchez, "EyeNED workstation: Development of a multi-modal vendor-independent application for annotation, spatial alignment and analysis of retinal images", Association for Research in Vision and Ophthalmology, 2019.
    Abstract Cited by ~1
13. 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
14. C. González-Gonzalo, B. Liefers, A. Vaidyanathan, H. van Zeeland, C. Klaver and C. Sánchez, "Opening the "black box" of deep learning in automated screening of eye diseases", Association for Research in Vision and Ophthalmology, 2019.
    Abstract Url
15. 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
16. 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
17. W. Bulten, H. Pinckaers, C. Hulsbergen-van de Kaa and G. Litjens, "Automated Gleason Grading of Prostate Biopsies Using Deep Learning", United States and Canadian Academy of Pathology (USCAP) 108th Annual Meeting, 2019.
    Abstract Cited by ~9
18. 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
19. N. Khalili, N. Lessmann, E. Turk, M. Viergever, M. Benders and I. Isgum, "Brain tissue segmentation in fetal MRI using convolutional neural networks with simulated intensity inhomogeneities", Annual Meeting of the International Society for Magnetic Resonance in Medicine, 2019.
    Abstract
20. B. Liefers, J. Colijn, C. González-Gonzalo, A. Vaidyanathan, H. van Zeeland, P. Mitchell, C. Klaver and C. Sánchez, "Prediction of areas at risk of developing geographic atrophy in color fundus images using deep learning", Association for Research in Vision and Ophthalmology, 2019.
    Abstract
21. H. Pinckaers and G. Litjens, "Training convolutional neural networks with megapixel images", Medical Imaging with Deep Learning, 2018.
    Abstract arXiv
22. 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
23. 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
24. 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
25. M. Silva, G. Capretti, N. Sverzellati, C. Jacobs, F. Ciompi, B. van Ginneken, C. Schaefer-Prokop, A. Marchianò and U. Pastorino, "Subsolid and part-solid nodules in lung cancer screening: comparison between visual and computer-aided detection", European Congress of Radiology, 2017.
    Abstract
26. M. Silva, G. Capretti, N. Sverzellati, C. Jacobs, F. Ciompi, B. van Ginneken, C. Schaefer-Prokop, M. Prokop, A. Marchiano and U. Pastorino, "Non-solid and Part-solid Nodules: Comparison Between Visual and Computer Aided Detection", World Congress of Thoracic Imaging, 2017.
    Abstract
27. F. Ciompi, K. Chung, A. Setio, S. van Riel, E. Scholten, P. Gerke, C. Jacobs, U. Pastorino, A. Marchiano, M. Wille, M. Prokop and B. van Ginneken, "Pulmonary nodule type classification with convolutional networks", Medical Image Computing and Computer-Assisted Intervention, 2016.
    Abstract
28. 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
29. S. van Riel, F. Ciompi, M. Wille, E. Scholten, A. Dirksen, K. Chung, M. Prokop, C. Schaefer-Prokop and B. van Ginneken, "Comparing LungRADS and the McWilliams nodule malignancy score: which approach works best to select screen detected pulmonary nodules for more aggressive followup?", Annual Meeting of the Radiological Society of North America, 2015.
    Abstract
30. K. Chung, E. Scholten, S. van Riel, F. Ciompi, P. de Jong, M. Wille, M. Prokop, B. van Ginneken and C. Schaefer-Prokop, "Differentiation of persistent and transient subsolid nodules: does morphology help?", European Congress of Radiology, 2015;85(3):648-652.
    Abstract
31. S. van Riel, F. Ciompi, M. Wille, E. Scholten, N. Sverzellati, S. Rossi, A. Dirksen, M. Brink, R. Wittenberg, M. Naqibullah, M. Prokop, C. Schaefer-Prokop and B. van Ginneken, "Can morphological features differentiate between malignant and benign pulmonary nodules, detected in a screen setting?", Annual Meeting of the Radiological Society of North America, 2015.
    Abstract
32. R. Arntz, S. van den Broek, L. Rutten-Jacobs, N. Maaijwee, I. van Uden, M. Ghafoorian, B. Platel, E. van Dijk and F. de Leeuw, "Small vessel disease after stroke at young age: the FUTURE-study", European Stroke Organization, 2015.
    Abstract
33. S. van Riel, F. Ciompi, M. Wille, M. Naqibullah, E. Scholten, C. Schaefer-Prokop and B. van Ginneken, "Lung-RADS versus the McWilliams nodule malignancy score for risk prediction: an evaluation using lesions from the DLCST Trial", World Conference on Lung Cancer, 2015.
    Abstract
34. J. Charbonnier, M. Brink, F. Ciompi, E. Scholten, C. Schaefer-Prokop and E. Van Rikxoort, "Automatic Separation and Classification of Arteries and Veins in Non-Contrast Thoracic CT Scans", Annual Meeting of the Radiological Society of North America, 2015.
    Abstract Url
35. F. Ciompi, B. de Hoop, C. Jacobs, M. Prokop, P. a de Jong and B. van Ginneken, "Automatic Classification of Perifissural Pulmonary Nodules in Thoracic CT Images", Annual Meeting of the Radiological Society of North America, 2014.
    Abstract
36. E. Vos, T. Kobus, G. Litjens, T. Hambrock, C. - van de Kaa, M. Maas and T. Scheenen, "Multiparametric MR imaging for the assessment of prostate cancer aggressiveness at 3 Tesla", Annual Meeting of the International Society for Magnetic Resonance in Medicine, 2014.
    Abstract
37. G. Litjens, N. Karssemeijer, J. Barentsz and H. Huisman, "Computer-aided Detection of Prostate Cancer in Multi-parametric Magnetic Resonance Imaging", Annual Meeting of the Radiological Society of North America, 2014.
    Abstract
38. G. Litjens, J. Barentsz, N. Karssemeijer and H. Huisman, "Initial prospective evaluation of the prostate imaging reporting and data standard (PI-RADS): Can it reduce unnecessary MR guided biopsies?", Annual Meeting of the Radiological Society of North America, 2013.
    Abstract
39. M. Maas, M. Koopman, G. Litjens, A. Wright, K. Selnas, I. Gribbestad, M. Haider, K. Macura, D. Margolis, B. Kiefer, J. Fütterer and T. Scheenen, "Prostate Cancer localization with a Multiparametric MR Approach (PCaMAP): initial results of a multi-center study", Annual Meeting of the International Society for Magnetic Resonance in Medicine, 2013.
    Abstract Cited by ~2
40. E. Vos, G. Litjens, T. Kobus, T. Hambrock, C. van de Hulsbergen Kaa, H. Huisman and T. Scheenen, "Dynamic contrast enhanced MR imaging for the assessment of prostate cancer aggressiveness at 3T", Annual Meeting of the International Society for Magnetic Resonance in Medicine, 2012.
    Abstract
41. G. Litjens, J. Barentsz, N. Karssemeijer and H. Huisman, "Computerized characterization of central gland lesions using texture and relaxation features from T2-weighted prostate MRI", Annual Meeting of the Radiological Society of North America, 2012.
    Abstract
42. M. Schouten, K. Nagel, T. Hambrock, C. Hoeks, G. Litjens, J. Barentsz and J. Fütterer, "Differentiation of Normal Prostate Tissue, Prostatitis, and Prostate Cancer: Correlation between Diffusion-weighted Imaging and MR-guided Biopsy", Annual Meeting of the Radiological Society of North America, 2011.
    Abstract
43. G. Litjens, J. Barentsz, N. Karssemeijer and H. Huisman, "Zone-specific Automatic Computer-aided Detection of Prostate Cancer in MRI", Annual Meeting of the Radiological Society of North America, 2011.
    Abstract
44. O. Debats, T. Hambrock, G. Litjens, H. Huisman and J. Barentsz, "Detection of Lymph Node Metastases with Ferumoxtran-10 vs Ferumoxytol", Annual Meeting of the Radiological Society of North America, 2011.
    Abstract
45. G. Litjens, M. Heisen, J. Buurman, A. Wood, M. Medved, G. Karczmar and B. Haar-Romeny, "T1 Quantification: Variable Flip Angle Method vs Use of Reference Phantom", Annual Meeting of the Radiological Society of North America, 2009.
    Abstract

PhD theses

1. W. Bulten, "Artificial intelligence as a digital fellow in pathology: Human-machine synergy for improved prostate cancer diagnosis", PhD thesis, 2022.
   Abstract Url
2. D. Tellez, "Advancing computational pathology with deep learning: from patches to gigapixel image-level classification", PhD thesis, 2021.
   Abstract Url
3. M. Balkenhol, "Tissue-based biomarker assessment for predicting prognosis of triple negative breast cancer: the additional value of artificial intelligence", PhD thesis, 2020.
   Abstract Url
4. B. Bejnordi, "Histopathological diagnosis of breast cancer using machine learning", PhD thesis, 2017.
   Abstract Url
5. J. Charbonnier, "Segmentation & quantification of airways and blood vessels in chest CT", PhD thesis, 2017.
   Abstract Url
6. G. Litjens, "Computerized detection of cancer in multi-parametric prostate MRI", PhD thesis, 2015.
   Abstract Url
7. F. Ciompi, "Multi-Class Learning for Vessel Characterization in Intravascular Ultrasound", PhD thesis, 2012.
   Abstract Url

Master theses

1. K. Faryna, "Brain MRI synthesis via pathology factorization and adversarial cycle-consistent learning for data augmentation", Master thesis, 2020.
   Abstract
2. J. Spronck, "Multi conditional lung nodule synthesis for improved nodule malignancy classification in Computed Tomography scans", Master thesis, 2020.
   Abstract Url
3. L. van Eekelen, "Deep learning-based analysis of bone marrow histopathology images", Master thesis, 2020.
   Abstract
4. T. Payer, "AI-assisted PD-L1 scoring in non-small-cell lung cancer", Master thesis, 2020.
   Abstract
5. M. van Rijthoven, "Cancer research in digital pathology using convolutional neural networks", Master thesis, 2019.
   Abstract
6. D. Geijs, "Tumor segmentation in fluorescent TNBC immunohistochemical multiplex images using deep learning", Master thesis, 2019.
   Abstract
7. G. Mooij, "Using GANs to synthetically stain histopathological images to generate training data for automatic mitosis detection in breast tissue", Master thesis, 2019.
   Abstract
8. P. Sonsma, "Lymphocyte detection in hematoxylin-eosin stained histopathological images of breast cancer", Master thesis, 2019.
   Abstract
9. E. Stoelinga, "Extracting biomarkers from hematoxylin-eosin stained histopathological images of lung cancer", Master thesis, 2019.
   Abstract
10. J. Winkens, "Out-of-distribution detection for computational pathology with multi-head ensembles", Master thesis, 2019.
    Abstract
11. M. den Boer, "Automated structure segmentation and lymphocyte detection in kidney transplant whole slide images using a convolutional neural network", Master thesis, 2018.
    Abstract
12. G. Litjens, "Pharmacokinetic modeling in breast cancer MRI", Master thesis, 2009.
    Abstract