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.
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.
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.
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.
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",
Scientific Reports,
2016;6:26286.
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.
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.
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.
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.
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.
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.
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.
D. Tellez, G. Litjens, P. Bándi, 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.
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.
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.
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",
Scientific Reports,
2017(46479).
G. Litjens, P. Bándi, B. Ehteshami Bejnordi, O. Geessink, M. Balkenhol, P. Bult, A. Halilovic, M. Hermsen, R. van de Loo, R. Vogels, Q. Manson, N. Stathonikos, A. Baidoshvili, P. van Diest, C. Wauters, M. van Dijk and J. van der Laak, "1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset",
GigaScience,
2018;7(6):1-8.
B. 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.
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.
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.
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.
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.
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.
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.
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",
Scientific Reports,
2017;7(1):5110.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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",
Scientific Reports,
2019;9(1).
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.
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.
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.
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,
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F. Zanjani, S. Zinger, B. Bejnordi, J. van der Laak and P. de With, "Stain normalization of histopathology images using generative adversarial networks",
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018),
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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,
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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,
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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,
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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,
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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,
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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,
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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
B. Nijhof, A. Castells-Nobau, L. Wolf, J. Scheffer-de Gooyert, I. Monedero, L. Torroja, L. Coromina, J. van der Laak and A. Schenck, "A New Fiji-Based Algorithm That Systematically Quantifies Nine Synaptic Parameters Provides Insights into Drosophila NMJ Morphometry",
PLOS Computational Biology,
2016;12:e1004823.
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.
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.
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.
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.
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.
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.
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.
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.
L. Maier-Hein, A. Reinke, P. Godau, M. Tizabi, F. Buettner, E. Christodoulou, B. Glocker, F. Isensee, J. Kleesiek, M. Kozubek, M. Reyes, M. Riegler, M. Wiesenfarth, A. Kavur, C. Sudre, M. Baumgartner, M. Eisenmann, D. Heckmann-Nötzel, T. Rädsch, L. Acion, M. Antonelli, T. Arbel, S. Bakas, A. Benis, M. Blaschko, M. Cardoso, V. Cheplygina, B. Cimini, G. Collins, K. Farahani, L. Ferrer, A. Galdran, B. van Ginneken, R. Haase, D. Hashimoto, M. Hoffman, M. Huisman, P. Jannin, C. Kahn, D. Kainmueller, B. Kainz, A. Karargyris, A. Karthikesalingam, F. Kofler, A. Kopp-Schneider, A. Kreshuk, T. Kurc, B. Landman, G. Litjens, A. Madani, K. Maier-Hein, A. Martel, P. Mattson, E. Meijering, B. Menze, K. Moons, H. Müller, B. Nichyporuk, F. Nickel, J. Petersen, N. Rajpoot, N. Rieke, J. Saez-Rodriguez, C. Sánchez, S. Shetty, M. van Smeden, R. Summers, A. Taha, A. Tiulpin, S. Tsaftaris, B. Van Calster, G. Varoquaux and P. Jäger, "Metrics reloaded: recommendations for image analysis validation",
Nature Methods,
2024;21:195-212.
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.
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.
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.
T. de Bel, M. Hermsen, J. van der Laak, G. Litjens, B. Smeets and L. Hilbrands, "Automatic segmentation of histopathological slides of renal tissue using deep learning",
Medical Imaging 2018: Digital Pathology,
2018.
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.
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.
Z. Li, J. Zhang, T. Tan, X. Teng, X. Sun, H. Zhao, L. Liu, Y. Xiao, B. Lee, Y. Li, Q. Zhang, S. Sun, Y. Zheng, J. Yan, N. Li, Y. Hong, J. Ko, H. Jung, Y. Liu, Y. Chen, C. Wang, V. Yurovskiy, P. Maevskikh, V. Khanagha, Y. Jiang, L. Yu, Z. Liu, D. Li, P. Schuffler, Q. Yu, H. Chen, Y. Tang and G. Litjens, "Deep Learning Methods for Lung Cancer Segmentation in Whole-Slide Histopathology Images--The ACDC@LungHP Challenge 2019",
IEEE Journal of Biomedical and Health Informatics,
2021;25:429-440.
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.
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.
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).
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.
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.
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.
F. Zanjani, S. Zinger, B. Bejnordi, J. van der Laak and P. de With, "Histopathology stain-color normalization using deep generative models",
Medical Imaging with Deep Learning,
2018.
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.
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.
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.
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.
M. Yousif, P. van Diest, A. Laurinavicius, D. Rimm, J. van der Laak, A. Madabhushi, S. Schnitt and L. Pantanowitz, "Artificial intelligence applied to breast pathology",
Virchows Archiv,
2021;480:191-209.
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.
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.
N. Marini, S. Marchesin, S. Otalora, 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.
K. Faryna, J. van der Laak and G. Litjens, "Tailoring automated data augmentation to H&E-stained histopathology",
Medical Imaging with Deep Learning,
2021.
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.
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.
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.
Z. Li, Z. Hu, J. Xu, T. Tan, H. Chen, Z. Duan, P. Liu, J. Tang, G. Cai, Q. Ouyang, Y. Tang, G. Litjens and Q. Li, "Computer-aided diagnosis of lung carcinoma using deep learning - a pilot study",
arXiv:1803.05471,
2018.
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.
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.