J. Lotz, N. Weiss, J. van der Laak and S. Heldmann, "Comparison of consecutive and restained sections for image registration in histopathology",
Journal of Medical Imaging,
2023;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.
Y. Jiao, J. van der Laak, S. Albarqouni, Z. Li, T. Tan, A. Bhalerao, J. Ma, J. Sun, J. Pocock, J. Pluim, N. Koohbanani, R. Bashir, S. Raza, S. Liu, S. Graham, S. Wetstein, S. Khurram, T. Watson, N. Rajpoot, M. Veta and F. Ciompi, "LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset",
IEEE Journal of Biomedical and Health Informatics,
2023:1-12.
J. Linmans, E. Hoogeboom, J. van der Laak and G. Litjens, "The Latent Doctor Model for Modeling Inter-Observer Variability",
IEEE Journal of Biomedical and Health Informatics,
2023:1-12.
J. Swillens, I. Nagtegaal, S. Engels, A. Lugli, R. Hermens and J. van der Laak, "Pathologists' first opinions on barriers and facilitators of computational pathology adoption in oncological pathology: an international study",
Oncogene,
2023;42:2816-2827.
S. Dooper, H. Pinckaers, W. Aswolinskiy, K. Hebeda, S. Jarkman, J. van der Laak and G. Litjens, "Gigapixel end-to-end training using streaming and attention",
Medical Image Analysis,
2023;88:102881.
J. Bokhorst, I. Nagtegaal, F. Fraggetta, S. Vatrano, W. Mesker, M. Vieth, J. van der Laak and F. Ciompi, "Deep learning for multi-class semantic segmentation enables colorectal cancer detection and classification in digital pathology images",
Scientific Reports,
2023;13:8398.
A. van der Kamp, T. de Bel, L. van Alst, J. Rutgers, M. van den Heuvel-Eibrink, A. Mavinkurve-Groothuis, J. van der Laak and R. de Krijger, "Automated Deep Learning-Based Classification of Wilms Tumor Histopathology",
Cancers,
2023;15:2656.
J. Bokhorst, I. Nagtegaal, I. Zlobec, H. Dawson, K. Sheahan, F. Simmer, R. Kirsch, M. Vieth, A. Lugli, J. van der Laak and F. Ciompi, "Semi-Supervised Learning to Automate Tumor Bud Detection in Cytokeratin-Stained Whole-Slide Images of Colorectal Cancer",
Cancers,
2023;15(7):2079.
J. Bogaerts, M. van Bommel, R. Hermens, M. Steenbeek, J. de Hullu, J. van der Laak, M. Simons and S. consortium, "Consensus based recommendations for the diagnosis of serous tubal intraepithelial carcinoma: an international Delphi study",
Histopathology,
2023;83:67-79.
A. Baidoshvili, M. Khacheishvili, J. van der Laak and P. van Diest, "A whole-slide imaging based workflow reduces the reading time of pathologists",
Pathology International,
2023;73:127-134.
J. Linmans, S. Elfwing, J. van der Laak and G. Litjens, "Predictive uncertainty estimation for out-of-distribution detection in digital pathology.",
Medical Image Analysis,
2023;83:102655.
M. Polack, M. Smit, S. Crobach, V. Terpstra, A. Roodvoets, E. Meershoek-Klein Kranenbarg, E. Dequeker, J. van der Laak, R. Tollenaar, H. van Krieken and W. Mesker, "Uniform Noting for International application of the Tumor-stroma ratio as Easy Diagnostic tool: The UNITED study - An update",
European Journal of Surgical Oncology,
2023;49:e132-e133.
D. Page, G. Broeckx, C. Jahangir, S. Verbandt, R. Gupta, J. Thagaard, R. Khiroya, Z. Kos, K. Abduljabbar, G. Acosta Haab, B. Acs, G. Akturk, J. Almeida, I. Alvarado-Cabrero, F. Azmoudeh-Ardalan, S. Badve, N. Baharun, E. Bellolio, V. Bheemaraju, K. Blenman, L. Mendonça Botinelly Fujimoto, N. Bouchmaa, O. Burgues, M. Cheang, F. Ciompi, L. Cooper, A. Coosemans, G. Corredor, F. Dantas Portela, F. Deman, S. Demaria, S. Dudgeon, M. Elghazawy, S. Ely, C. Fernandez-Martín, S. Fineberg, S. Fox, W. Gallagher, J. Giltnane, S. Gnjatic, P. Gonzalez-Ericsson, A. Grigoriadis, N. Halama, M. Hanna, A. Harbhajanka, A. Hardas, S. Hart, J. Hartman, S. Hewitt, A. Hida, H. Horlings, Z. Husain, E. Hytopoulos, S. Irshad, E. Janssen, M. Kahila, T. Kataoka, K. Kawaguchi, D. Kharidehal, A. Khramtsov, U. Kiraz, P. Kirtani, L. Kodach, K. Korski, A. Kovács, A. Laenkholm, C. Lang-Schwarz, D. Larsimont, J. Lennerz, M. Lerousseau, X. Li, A. Ly, A. Madabhushi, S. Maley, V. Manur Narasimhamurthy, D. Marks, E. McDonald, R. Mehrotra, S. Michiels, F. Minhas, S. Mittal, D. Moore, S. Mushtaq, H. Nighat, T. Papathomas, F. Penault-Llorca, R. Perera, C. Pinard, J. Pinto-Cardenas, G. Pruneri, L. Pusztai, A. Rahman, N. Rajpoot, B. Rapoport, T. Rau, J. Reis-Filho, J. Ribeiro, D. Rimm, A. Vincent-Salomon, M. Salto-Tellez, J. Saltz, S. Sayed, K. Siziopikou, C. Sotiriou, A. Stenzinger, M. Sughayer, D. Sur, F. Symmans, S. Tanaka, T. Taxter, S. Tejpar, J. Teuwen, E. Thompson, T. Tramm, W. Tran, J. van der Laak, P. van Diest, G. Verghese, G. Viale, M. Vieth, N. Wahab, T. Walter, Y. Waumans, H. Wen, W. Yang, Y. Yuan, S. Adams, J. Bartlett, S. Loibl, C. Denkert, P. Savas, S. Loi, R. Salgado and E. Specht Stovgaard, "Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer",
The Journal of Pathology,
2023;260:514-532.
J. Bokhorst, F. Ciompi, S. Öztürk, A. Oguz Erdogan, M. Vieth, H. Dawson, R. Kirsch, F. Simmer, K. Sheahan, A. Lugli, I. Zlobec, J. van der Laak and I. Nagtegaal, "Fully Automated Tumor Bud Assessment in Hematoxylin and Eosin-Stained Whole Slide Images of Colorectal Cancer",
Modern Pathology,
2023;36:100233.
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. 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.
J. Thagaard, G. Broeckx, D. Page, C. Jahangir, S. Verbandt, Z. Kos, R. Gupta, R. Khiroya, K. Abduljabbar, G. Acosta Haab, B. Acs, G. Akturk, J. Almeida, I. Alvarado-Cabrero, M. Amgad, F. Azmoudeh-Ardalan, S. Badve, N. Baharun, E. Balslev, E. Bellolio, V. Bheemaraju, K. Blenman, L. Mendonça Botinelly Fujimoto, N. Bouchmaa, O. Burgues, A. Chardas, M. U Chon Cheang, F. Ciompi, L. Cooper, A. Coosemans, G. Corredor, A. Dahl, F. Dantas Portela, F. Deman, S. Demaria, J. Doré Hansen, S. Dudgeon, T. Ebstrup, M. Elghazawy, C. Fernandez-Martín, S. Fox, W. Gallagher, J. Giltnane, S. Gnjatic, P. Gonzalez-Ericsson, A. Grigoriadis, N. Halama, M. Hanna, A. Harbhajanka, S. Hart, J. Hartman, S. Hauberg, S. Hewitt, A. Hida, H. Horlings, Z. Husain, E. Hytopoulos, S. Irshad, E. Janssen, M. Kahila, T. Kataoka, K. Kawaguchi, D. Kharidehal, A. Khramtsov, U. Kiraz, P. Kirtani, L. Kodach, K. Korski, A. Kovács, A. Laenkholm, C. Lang-Schwarz, D. Larsimont, J. Lennerz, M. Lerousseau, X. Li, A. Ly, A. Madabhushi, S. Maley, V. Manur Narasimhamurthy, D. Marks, E. McDonald, R. Mehrotra, S. Michiels, F. Minhas, S. Mittal, D. Moore, S. Mushtaq, H. Nighat, T. Papathomas, F. Penault-Llorca, R. Perera, C. Pinard, J. Pinto-Cardenas, G. Pruneri, L. Pusztai, A. Rahman, N. Rajpoot, B. Rapoport, T. Rau, J. Reis-Filho, J. Ribeiro, D. Rimm, A. Roslind, A. Vincent-Salomon, M. Salto-Tellez, J. Saltz, S. Sayed, E. Scott, K. Siziopikou, C. Sotiriou, A. Stenzinger, M. Sughayer, D. Sur, S. Fineberg, F. Symmans, S. Tanaka, T. Taxter, S. Tejpar, J. Teuwen, E. Thompson, T. Tramm, W. Tran, J. van der Laak, P. van Diest, G. Verghese, G. Viale, M. Vieth, N. Wahab, T. Walter, Y. Waumans, H. Wen, W. Yang, Y. Yuan, R. Zin, S. Adams, J. Bartlett, S. Loibl, C. Denkert, P. Savas, S. Loi, R. Salgado and E. Specht Stovgaard, "Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer",
The Journal of Pathology,
2023;260:498-513.
C. Grisi, G. Litjens and J. van der Laak, "Hierarchical Vision Transformers for Context-Aware Prostate Cancer Grading in Whole Slide Images",
arXiv:2312.12619,
2023.
J. Spronck, T. Gelton, L. van Eekelen, J. Bogaerts, L. Tessier, M. van Rijthoven, L. van der Woude, M. van den Heuvel, W. Theelen, J. van der Laak and F. Ciompi, "nnUNet meets pathology: bridging the gap for application to whole-slide images and computational biomarkers",
Medical Imaging with Deep Learning,
2023.
R. Lomans, J. van der Laak, I. Nagtegaal, F. Ciompi and R. van der Post, "Deep learning for multi-class cell detection in H&E-stained slides of diffuse gastric cancer",
European Congress of Pathology,
2023.
R. Leon-Ferre, J. Carter, D. Zahrieh, J. Sinnwell, R. Salgado, V. Suman, D. Hillman, J. Boughey, K. Kalari, F. Couch, J. Ingle, M. Balkenkohl, F. Ciompi, J. van der Laak and M. Goetz, "Abstract P2-11-34: Mitotic spindle hotspot counting using deep learning networks is highly associated with clinical outcomes in patients with early-stage triple-negative breast cancer who did not receive systemic therapy",
Cancer Research,
2023;83:P2-11-34-P2-11-34.
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.