R. Leon-Ferre, J. Carter, D. Zahrieh, J. Sinnwell, R. Salgado, V. Suman, D. Hillman, J. Boughey, K. Kalari, F. Couch, J. Ingle, M. Balkenhol, F. Ciompi, J. van der Laak and M. Goetz, "Automated mitotic spindle hotspot counts are highly associated with clinical outcomes in systemically untreated early-stage triple-negative breast cancer",
npj Breast Cancer,
2024;10.
W. Aswolinskiy, E. Munari, H. Horlings, L. Mulder, G. Bogina, J. Sanders, Y. Liu, A. van den Belt-Dusebout, L. Tessier, M. Balkenhol, M. Stegeman, J. Hoven, J. Wesseling, J. van der Laak, E. Lips and F. Ciompi, "PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning",
Breast Cancer Research,
2023;25.
P. Bándi, M. Balkenhol, M. van Dijk, M. Kok, B. van Ginneken, J. van der Laak and G. Litjens, "Continual learning strategies for cancer-independent detection of lymph node metastases",
Medical Image Analysis,
2023;85:102755.
M. D'Amato, M. Balkenhol, M. van Rijthoven, J. van der Laak and F. Ciompi, "On the robustness of regressing tumor percentage as an explainable detector in histopathology whole-slide images",
Medical Imaging with Deep Learning,
2023.
C. Mercan, M. Balkenhol, R. Salgado, M. Sherman, P. Vielh, W. Vreuls, A. Polonia, 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.
V. Bergshoeff, M. Balkenhol, A. Haesevoets, A. Ruland, M. Chenault, R. Nelissen, C. Peutz, R. Clarijs, J. der Van Laak, R. Takes, M. den Van Brekel, M. Van Velthuysen, F. Ramaekers, B. Kremer and E. Speel, "Evaluation Criteria for Chromosome Instability Detection by FISH to Predict Malignant Progression in Premalignant Glottic Laryngeal Lesions",
Cancers,
2022;14:3260.
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.
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.
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.
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.
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.
C. Mercan, M. Balkenhol, R. Salgado, M. Sherman, P. Vielh, W. Vreuls, A. Polonia, H. Horlings, W. Weichert, J. Carter, P. Bult, M. Christgen, C. Denkert, K. van de Vijver, J. van der Laak and F. Ciompi, "Automated Scoring of Nuclear Pleomorphism Spectrum with Pathologist-level Performance in Breast Cancer",
arXiv:2012.04974,
2020.
C. Mercan, M. Balkenhol, J. Laak and F. Ciompi, "Grading nuclear pleomorphism in breast cancer using deep learning",
European Congress of Pathology,
2020.
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.
M. Balkenhol, "Tissue-based biomarker assessment for predicting prognosis of triple negative breast cancer: the additional value of artificial intelligence",
PhD thesis,
2020.
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.
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.
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.
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.
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.
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, 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.
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.
Z. Swiderska-Chadaj, H. Pinckaers, M. van Rijthoven, M. Balkenhol, M. Melnikova, O. Geessink, Q. Manson, G. Litjens, J. van der Laak and F. Ciompi, "Convolutional Neural Networks for Lymphocyte detection in Immunohistochemically Stained Whole-Slide Images",
Medical Imaging with Deep Learning,
2018.
D. Tellez, M. Balkenhol, N. Karssemeijer, G. Litjens, J. van der Laak and F. Ciompi, "H&E stain augmentation improves generalization of convolutional networks for histopathological mitosis detection",
Medical Imaging,
2018;10581.
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.
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.
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.
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.
H. Kost, A. Homeyer, P. Bult, M. Balkenhol, J. van der Laak and H. Hahn, "A generic nuclei detection method for histopathological breast images",
SPIE Proceedings,
2016.