FRANCESCO CIOMPI

francescociompi.com | 2025

JOURNAL PAPERS

* shared authorship

M. D'Amato, J. van der Laak and F. Ciompi, ""No negatives needed": weakly-supervised regression for interpretable tumor detection in whole-slide histopathology images", arXiv:2502.21109, 2025. [Preprint]

Witali Aswolinskiy, Rachel S van der Post, Michiel Simons, Enrico Munari, Michela Campora, Carla Baronchelli, Laura Ardighieri, Simona Vatrano, Iris Nagtegaal, Jeroen van der Laak, Francesco Ciompi, "Attention-based whole-slide image compression achieves pathologist-level pre-screening of multi-organ routine histopathology biopsies", https://doi.org/10.1101/2024.12.17.24319180, 2025. [Preprint]

M. van Rijthoven, W. Aswolinskiy, L. Tessier, M. Balkenhol, J. Bogaerts, D. Drubay, L. Blesa, D. Peeters, E. Stovgaard, A. L\aenkholm , H. Haynes, L. Craciun, D. Larsimont, M. Amgad, L. Cooper, C. de Kock, V. Dechering, J. Lotz, N. Weiss, M. van Bockstal, C. Galant, E. Lips, H. Horlings, J. Wesseling, L. Mulder, S. van den Belt, K. Weber, P. Jank, C. Denkert, E. Munari, G. Bogina, C. Russ, A. Lemm, S. Loi, J. Douglas, S. Michiels, H. Joensuu, M. Fan, D. Lee, J. Ye, K. Byun, J. Kim, S. Xu, Z. Ji, F. Xie, J. Kuang, X. Chen, L. Chen, A. Tsakiroglou, R. Byers, M. Fergie, V. Ramanathan, A. Martel, A. Shephard, S. Ahmed Raza, M. Jahanifar, N. Rajpoot, S. Cho, D. Kim, H. Jang, C. Park, K. Kim, R. Donders, S. Maurits, M. Groeneveld, A. Mickan, J. Meakin, B. van Ginneken, R. Salgado, J. van der Laak and F. Ciompi, "Tumor-infiltrating lymphocytes in breast cancer through artificial intelligence: biomarker analysis from the results of the TIGER challenge", https://doi.org/10.1101/2025.02.28.25323078, 2025. [Preprint]

D. Höppener, W. Aswolinskiy, Z. Qian, D. Tellez, P. Nierop, M. Starmans, I. Nagtegaal, M. Doukas, J. de Wilt, D. Grünhagen, J. van der Laak, P. Vermeulen, F. Ciompi and C. Verhoef, "Classifying histopathological growth patterns for resected colorectal liver metastasis with a deep learning analysis", BJS Open, 2024;8.

N. Khalili and F. Ciompi, "Scaling data toward pan-cancer foundation models", Trends in Cancer, 2024;10:871-872.

E. Smeets, M. Trajkovic-Arsic, D. Geijs, S. Karakaya, M. van Zanten, L. Brosens, B. Feuerecker, M. Gotthardt, J. Siveke, R. Braren, F. Ciompi and E. Aarntzen, "Histology-Based Radiomics for [18F]FDG PET Identifies Tissue Heterogeneity in Pancreatic Cancer", Journal of Nuclear Medicine,

2024:jnumed.123.266262.

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.

A. Vos, L. Pijnenborg, S. van Vliet, L. Kodach, F. Ciompi, R. van der Post, F. Simmer and I. Nagtegaal, "Biological background of colorectal polyps and carcinomas with heterotopic ossification: A national study and literature review", Human Pathology, 2024;145:34-41.

E. Chelebian, C. Avenel, F. Ciompi and C. Wählby, "DEPICTER: Deep representation clustering for histology annotation", Computers in Biology and Medicine, 2024;170:108026. [Code]

C. Jahangir, D. Page, G. Broeckx, C. Gonzalez, C. Burke, C. Murphy, J. Reis-Filho, A. Ly, P. Harms, R. Gupta, M. Vieth, A. Hida, M. Kahila, Z. Kos, P. van Diest, S. Verbandt, J. Thagaard, R. Khiroya, K. Abduljabbar, G. Acosta Haab, B. Acs, S. Adams, J. Almeida, I. Alvarado-Cabrero, F. Azmoudeh-Ardalan, S. Badve, N. Baharun, E. Bellolio, V. Bheemaraju, K. Blenman, L. Mendonça Botinelly Fujimoto, O. Burgues, A. Chardas, M. Cheang, F. Ciompi, L. Cooper, A. Coosemans, G. Corredor, F. Dantas Portela, F. Deman, S. Demaria, S. Dudgeon, M. Elghazawy, C. Fernandez-Martín, S. Fineberg, S. Fox, J. Giltnane, S. Gnjatic, P. Gonzalez-Ericsson, A. Grigoriadis, N. Halama, M. Hanna, A. Harbhajanka, S. Hart, J. Hartman, S. Hewitt, H. Horlings, Z. Husain, S. Irshad, E. Janssen, T. Kataoka, K. Kawaguchi, A. Khramtsov, U. Kiraz, P. Kirtani, L. Kodach, K. Korski, G. Akturk, E. Scott, A. Kovács, A. L\aenkholm , C. Lang-Schwarz, D. Larsimont, J. Lennerz, M. Lerousseau, X. Li, A. Madabhushi, S. Maley, V. Manur Narasimhamurthy, D. Marks, E. McDonald, R. Mehrotra, S. Michiels, D. Kharidehal, 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, N. Rajpoot, B. Rapoport, T. Rau, J. Ribeiro, D. Rimm, A. Vincent-Salomon, J. Saltz, S. Sayed, E. Hytopoulos, S. Mahon, 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, G. Verghese, G. Viale, N. Wahab, T. Walter, Y. Waumans, H. Wen, W. Yang, Y. Yuan, J. Bartlett, S. Loibl, C. Denkert, P. Savas, S. Loi, E. Specht Stovgaard, R. Salgado, W. Gallagher and A. Rahman, "Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer", The Journal of Pathology, 2024;262:271-288.

L. van Eekelen, J. Spronck, M. Looijen-Salamon, S. Vos, E. Munari, I. Girolami, A. Eccher, B. Acs, C. Boyaci, G. Silva de Souza, M. Demirel-Andishmand, L. Dulce Meesters, D. Zegers, L. van der Woude, W. Theelen, M. van den Heuvel, K. Grünberg, B. van Ginneken, J. van der Laak and F. Ciompi, "Comparing deep learning and pathologist quantification of cell-level PD-L1 expression in non-small cell lung cancer whole-slide images", Nature Scientific Reports 14, 7136 (2024).

[PD-L1 algorithm] [Nuclei detection algorithm]

S. Vermorgen, T. Gelton, P. Bult, H. V.N. Kusters-Vandevelde, J. Hausnerová, K. Van de Vijver, B. Davidson, I. Marie Stefansson, L. F.S. Kooreman, A. Qerimi, J. Huvila, B. Gilks, M. Shahi, S. Zomer, C. Bartosch, J. M.A. Pijnenborg, J. Bulten, F. Ciompi* and M. Simons*, "Endometrial Pipelle Biopsy Computer-Aided Diagnosis: A Feasibility Study", Modern Pathology, Volume 37, Issue 2, 2024. [Data] [Evaluation platform]

M. van Rijthoven, S. Obahor, F. Pagliarulo, V. den Maries, P. Schraml, H. Moch, J. van der Laak, F. Ciompi* and K. Silina*, "Multi-resolution deep learning characterizes tertiary lymphoid structures and their prognostic relevance in solid tumors", Nature Communications Medicine, 2024. [Code] [Annotations] [Masks] [Model weights]

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. [Algorithm]

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. [Hackathon website] [Data]

J. Bokhorst, I. Nagtegaal, F. Fraggetta, S. Vatrano, W. Mesker, M. Vieth, J. van der Laak and F. Ciompi, "Deep learning for multi-class semantic segmentation enables colorectal cancer detection and classification in digital pathology images", Nature Scientific Reports, 2023;13:8398. [Algorithm]

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. [Algorithm]

N. Brouwer, A. Khan, J. Bokhorst, F. Ayatollahi, J. Hay, F. Ciompi, F. Simmer, N. Hugen, J. de Wilt, M. Berger, A. Lugli, I. Zlobec, J. Edwards and I. Nagtegaal, "The complexity of shapes; how the circularity of tumor nodules impacts prognosis in colorectal cancer", Modern Pathology, 2023:100376.

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.

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.

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.

L. Menotti, G. Silvello, M. Atzori, S. Boytcheva, F. Ciompi, G. Di Nunzio, F. Fraggetta, F. Giachelle, O. Irrera, S. Marchesin, N. Marini, H. Müller and T. Primov, "Modelling digital health data: The ExaMode ontology for computational pathology", Journal of Pathology Informatics, 2023;14:100332.

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.

[Challenge website]

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.

[Data] [Evaluation platform]

S. Marchesin, F. Giachelle, N. Marini, M. Atzori, S. Boytcheva, G. Buttafuoco, F. Ciompi, G. Di Nunzio, F. Fraggetta, O. Irrera, H. Muller, T. Primov, S. Vatrano and G. Silvello, "Empowering digital pathology applications through explainable knowledge extraction tools.", Journal of pathology informatics,2022;13:100139.

E. Munari, G. Querzoli, M. Brunelli, M. Marconi, M. Sommaggio, M. Cocchi, G. Martignoni, G. Netto, A. Calio, 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.

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.

M. Hermsen, F. Ciompi, A. Adefidipe, A. Denic, A. Dendooven, B. Smith, D. van Midden, J. Brasen, J. Kers, M. Stegall, P. Bándi, 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.

G. Litjens, F. Ciompi and J. van der Laak, "A Decade of GigaScience: The Challenges of Gigapixel Pathology Images.", GigaScience, 2022;11.

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.

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.

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).

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. [Download]

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.

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.

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.

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.

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.

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).

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).

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.

J. van der Laak, F. Ciompi and G. Litjens, "No pixel-level annotations needed", Nature Biomedical Engineering, 2019;3(11):855-856.

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.

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, 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. 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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, 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.

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.

C. Gatta and F. Ciompi, "Stacked sequential scale-space Taylor context", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014;36(8):1694-1700.

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.

F. Ciompi, O. Pujol and P. Radeva, "ECOC-DRF: Discriminative Random Fields based on Error-Correcting Output Codes", Pattern Recognition, 2014;47:2193-2204.

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.

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.

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.

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.

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.

CONFERENCE PAPERS

  1. C. Lems, D. Geijs, J. Bokhorst, M. Sülter, L. van Eekelen and F. Ciompi, "Color Deconvolution for Color-Agnostic and Cross-Modality Analysis of Immunohistochemistry Whole-Slide Images with Deep Learning", 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 2024:1-4.
  2. L. Borras Ferris, S. Püttmann, N. Marini, S. Vatrano, F. Fragetta, A. Caputo, F. Ciompi, M. Atzori and H. Müller, "A full pipeline to analyze lung histopathology images", Medical Imaging 2024: Digital and Computational Pathology, 2024.
  3. A. Polejowska, A. Boleij and F. Ciompi, "Histopathobiome - integrating histopathology and microbiome data via multimodal deep learning", Proceedings of the MICCAI Workshop on Computational Pathology,2024;254:203-213.
  4. N. Contreras, C. Grisi, W. Aswolinskiy, S. Vatrano, F. Fraggetta, I. Nagtegaal, M. D'Amato and F. Ciompi, "Benchmarking Hierarchical Image Pyramid Transformer for the Classification of Colon Biopsies and Polyps Histopathology Images", 2024 IEEE International Symposium on Biomedical Imaging (ISBI),2024:1-4.
  5. C. Tommasino, C. Russo, A. Rinaldi and F. Ciompi, ""HoVer-UNet": Accelerating Hovernet with Unet-Based Multi-Class Nuclei Segmentation Via Knowledge Distillation", 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 2024:1-4.
  6. S. Püttmann, L. Borras Ferris, N. Marini, W. Aswolinsky, S. Vatrano, F. Fragetta, I. Nagtegaal, C. van der Post, F. Ciompi, M. Atzori, C. Friedrich and H. Müller, "Automated classification of celiac disease in histopathological images: a multi-scale approach", Medical Imaging 2024: Computer-Aided Diagnosis,2024.
  7. 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.
  8. E. Chelebian and F. Ciompi, "Seeded iterative clustering for histology region identification", Learning Meaningful Representations of Life, NeurIPS 2022, 2022.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. 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.
  26. 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.
  27. 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.
  28. 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.
  29. 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.
  30. 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.
  31. 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).
  32. 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).
  33. 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.
  34. 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.
  35. 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.
  36. 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.
  37. 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.
  38. 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.
  39. 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.
  40. 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.
  41. 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.
  42. 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.
  43. 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.

ABSTRACTS

  1. S. de Jong, M. Groot, R. Verhoeven, E. van der Heijden and F. Ciompi, "Weakly supervised lung cancer detection on label-free intraoperative microscopy with higher harmonic generation", Medical Imaging with Deep Learning 2024.
  2. L. Eekelen, G. den Heuvel, L. Studer, J. Spronck, K. Grünberg, D. Zegers, J. der Laak, M. den Heuvel and F. Ciompi, "Immunotherapy response prediction for non-small cell lung cancer is improved by using cell-graphs of the tumor microenvironment", European Congress on Digital Pathology, 2024.
  3. M. D'Amato, A. Boden, P. van Diest, N. Stathonikos, H. Hoefling, F. Versaevel, G. Litjens, F. Ciompi and J. van der Laak, "Automated Quality Control in Histopathology through Artifact Segmentation", European Congress on Digital Pathology, 2024.
  4. A. Polejowska, F. Ayatollahi, A. Erdogan, F. Ciompi and A. Boleij, "Spirochetosis detection in colon histopathology images via fine-tuning and boosting techniques using foundation models", Medical Imaging with Deep Learning 2024.
  5. M. Stegeman, G. Bogina, E. Munari, J. van der Laak and F. Ciompi, "Vision Language Foundation Models for Scoring Tumor-Infiltrating Lymphocytes in Breast Cancer through Text Prompting", European Congress on Digital Pathology, 2024.
  6. 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.
  7. R. Lomans, R. van der Post and F. Ciompi, "Interactive Cell Detection in H&E-stained slides of Diffuse Gastric Cancer", Medical Imaging with Deep Learning, 2023.
  8. 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.
  9. B. Guevara, N. Marini, S. Marchesin, W. Aswolinskiy, R. Schlimbach, D. Podareanu and F. Ciompi, "Caption generation from histopathology whole-slide images using pre-trained transformers", Medical Imaging with Deep Learning, 2023.
  10. 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.
  11. 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.
  12. 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.
  13. L. van Eekelen, E. Munari, I. Girolami, A. Eccher, J. van der Laak, K. Grunberg, 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.
  14. 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.
  15. 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.
  16. 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.
  17. C. Mercan, M. Balkenhol, J. Laak and F. Ciompi, "Grading nuclear pleomorphism in breast cancer using deep learning", European Congress of Pathology, 2020.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. 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.
  26. 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.
  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.
  28. 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.
  29. 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.
  30. 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.
  31. 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.
  32. 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.
  33. 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.