Publications of Francesco Ciompi

Papers in international journals

  1. J. van der Laak, G. Litjens and F. Ciompi, "Deep learning in histopathology: the path to the clinic.", Nature Medicine, 2021;27(5):775-784.
    Abstract DOI PMID
  2. F. Faita, T. Oranges, N. Di Lascio, F. Ciompi, S. Vitali, G. Aringhieri, A. Janowska, M. Romanelli and V. Dini, "Ultra-high-frequency ultrasound and machine learning approaches for the differential diagnosis of melanocytic lesions.", Experimental Dermatology, 2021.
    Abstract DOI PMID
  3. M. Balkenhol, F. Ciompi, Ż. Świderska-Chadaj, R. van de Loo, M. Intezar, I. Otte-Höller, 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.", BREAST, 2021;56:78-87.
    Abstract DOI PMID
  4. M. van Rijthoven, M. Balkenhol, K. Silina, J. van der Laak and F. Ciompi, "HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images", Medical Image Analysis, 2021;68:101890.
    Abstract DOI PMID GitHub Algorithm
  5. D. Tellez, G. Litjens, J. van der Laak and F. Ciompi, "Neural Image Compression for Gigapixel Histopathology Image Analysis.", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021;43(2):567-578.
    Abstract DOI PMID
  6. Z. Kos, A. Roblin, R. Kim, S. Michiels, B. Gallas, W. Chen, K. van de Vijver, S. Goel, S. Adams, S. Demaria, G. Viale, T. Nielsen, S. Badve, W. Symmans, C. Sotiriou, D. Rimm, S. Hewitt, C. Denkert, S. Loibl, S. Luen, J. Bartlett, P. Savas, G. Pruneri, D. Dillon, M. Cheang, A. Tutt, J. Hall, M. Kok, H. Horlings, A. Madabhushi, J. van der Laak, F. Ciompi, A. Laenkholm, E. Bellolio, T. Gruosso, S. Fox, J. Araya, G. Floris, J. Hudeček, L. Voorwerk, A. Beck, J. Kerner, D. Larsimont, S. Declercq, G. den Eynden, L. Pusztai, A. Ehinger, W. Yang, K. AbdulJabbar, Y. Yuan, R. Singh, C. Hiley, M. al Bakir, A. Lazar, S. Naber, S. Wienert, M. Castillo, G. Curigliano, M. Dieci, F. André, C. Swanton, J. Reis-Filho, J. Sparano, E. Balslev, I. Chen, E. Stovgaard, K. Pogue-Geile, K. Blenman, F. Penault-Llorca, S. Schnitt, S. Lakhani, A. Vincent-Salomon, F. Rojo, J. Braybrooke, M. Hanna, M. Soler-Monsó, D. Bethmann, C. Castaneda, K. Willard-Gallo, A. Sharma, H. Lien, S. Fineberg, J. Thagaard, L. Comerma, P. Gonzalez-Ericsson, E. Brogi, S. Loi, J. Saltz, F. Klaushen, L. Cooper, M. Amgad, D. Moore and R. Salgado, "Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer", npj Breast Cancer, 2020;6(1).
    Abstract DOI PMID Cited by ~1
  7. M. Amgad, A. Stovgaard, E. Balslev, J. Thagaard, W. Chen, S. Dudgeon, A. Sharma, J. Kerner, C. Denkert, Y. Yuan, K. AbdulJabbar, S. Wienert, P. Savas, L. Voorwerk, A. Beck, A. Madabhushi, J. Hartman, M. Sebastian, H. Horlings, J. Hudeček, F. Ciompi, D. Moore, R. Singh, E. Roblin, M. Balancin, M. Mathieu, J. Lennerz, P. Kirtani, I. Chen, J. Braybrooke, G. Pruneri, S. Demaria, S. Adams, S. Schnitt, S. Lakhani, F. Rojo, L. Comerma, S. Badve, M. Khojasteh, W. Symmans, C. Sotiriou, P. Gonzalez-Ericsson, K. Pogue-Geile, R. Kim, D. Rimm, G. Viale, S. Hewitt, J. Bartlett, F. Penault-Llorca, S. Goel, H. Lien, S. Loibl, Z. Kos, S. Loi, M. Hanna, S. Michiels, M. Kok, T. Nielsen, A. Lazar, Z. Bago-Horvath, L. Kooreman, J. van der Laak, J. Saltz, B. Gallas, U. Kurkure, M. Barnes, R. Salgado and L. Cooper, "Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group", npj Breast Cancer, 2020;6(1).
    Abstract DOI PMID Cited by ~2
  8. J. Bokhorst, A. Blank, A. Lugli, I. Zlobec, H. Dawson, M. Vieth, L. Rijstenberg, S. Brockmoeller, M. Urbanowicz, J. Flejou, R. Kirsch, F. Ciompi, J. van der Laak and I. Nagtegaal, "Assessment of individual tumor buds using keratin immunohistochemistry: moderate interobserver agreement suggests a role for machine learning", Modern Pathology, 2019.
    Abstract DOI PMID Cited by ~2
  9. J. van der Laak, F. Ciompi and G. Litjens, "No pixel-level annotations needed", Nature Biomedical Engineering, 2019;3(11):855-856.
    Abstract DOI PMID Cited by ~2
  10. Z. Swiderska-Chadaj, H. Pinckaers, M. van Rijthoven, M. Balkenhol, M. Melnikova, O. Geessink, Q. Manson, M. Sherman, A. Polonia, J. Parry, M. Abubakar, G. Litjens, J. van der Laak and F. Ciompi, "Learning to detect lymphocytes in immunohistochemistry with deep learning", Medical Image Analysis, 2019;58:101547.
    Abstract DOI PMID Cited by ~9
  11. D. Tellez, G. Litjens, P. Bandi, W. Bulten, J. Bokhorst, F. Ciompi and J. van der Laak, "Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology", Medical Image Analysis, 2019;58:101544.
    Abstract DOI PMID Cited by ~28
  12. G. Litjens, F. Ciompi, J. Wolterink, B. de Vos, T. Leiner, J. Teuwen and I. Isgum, "State-of-the-Art Deep Learning in Cardiovascular Image Analysis", JACC Cardiovascular Imaging, 2019;12(8 Pt 1):1549-1565.
    Abstract DOI PMID Cited by ~34
  13. M. Balkenhol, D. Tellez, W. Vreuls, P. Clahsen, H. Pinckaers, F. Ciompi, P. Bult and J. van der Laak, "Deep learning assisted mitotic counting for breast cancer", Laboratory Investigation, 2019.
    Abstract DOI PMID Cited by ~7
  14. M. Balkenhol, P. Bult, D. Tellez, W. Vreuls, P. Clahsen, F. Ciompi and J. van der Laak, "Deep learning and manual assessment show that the absolute mitotic count does not contain prognostic information in triple negative breast cancer", Cellular Oncology, 2019;42:4555-4569.
    Abstract DOI PMID Cited by ~8
  15. M. Veta, Y. Heng, N. Stathonikos, B. Bejnordi, F. Beca, T. Wollmann, K. Rohr, M. Shah, D. Wang, M. Rousson, M. Hedlund, D. Tellez, F. Ciompi, E. Zerhouni, D. Lanyi, M. Viana, V. Kovalev, V. Liauchuk, H. Phoulady, T. Qaiser, S. Graham, N. Rajpoot, E. Sjoblom, J. Molin, K. Paeng, S. Hwang, S. Park, Z. Jia, E. Chang, Y. Xu, A. Beck, P. van Diest and J. Pluim, "Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge", Medical Image Analysis, 2019;54(5):111-121.
    Abstract DOI PMID Cited by ~56
  16. O. Geessink, A. Baidoshvili, J. Klaase, B. Ehteshami Bejnordi, G. Litjens, G. van Pelt, W. Mesker, I. Nagtegaal, F. Ciompi and J. van der Laak, "Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer", Cellular Oncology, 2019:1-11.
    Abstract DOI PMID Cited by ~8
  17. S. Balocco, F. Ciompi, J. Rigla, X. Carrillo, J. Mauri and P. Radeva, "Assessment Of Intra-coronary Stent Location And Extension In Intravascular Ultrasound Sequences", Medical Physics, 2018;46(2):484-493.
    Abstract DOI PMID Cited by ~1
  18. M. Silva, M. Prokop, C. Jacobs, G. Capretti, N. Sverzellati, F. Ciompi, B. van Ginneken, C. Schaefer-Prokop, C. Galeone, A. Marchiano and U. Pastorino, "Long-term Active Surveillance of Screening Detected Subsolid Nodules is a Safe Strategy to Reduce Overtreatment", Journal of Thoracic Oncology, 2018;13:1454-1463.
    Abstract DOI PMID Cited by ~15
  19. D. Tellez, M. Balkenhol, I. Otte-Holler, R. van de Loo, R. Vogels, P. Bult, C. Wauters, W. Vreuls, S. Mol, N. Karssemeijer, G. Litjens, J. van der Laak and F. Ciompi, "Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks", IEEE Transactions on Medical Imaging, 2018;37(9):2126 - 2136.
    Abstract DOI PMID Cited by ~45
  20. M. Silva, C. Schaefer-Prokop, C. Jacobs, G. Capretti, F. Ciompi, B. van Ginneken, U. Pastorino and N. Sverzellati, "Detection of Subsolid Nodules in Lung Cancer Screening: Complementary Sensitivity of Visual Reading and Computer-Aided Diagnosis", Investigative Radiology, 2018;53(8):441-449.
    Abstract DOI PMID Cited by ~13
  21. K. Chung, F. Ciompi, J. Scholten E. Th. Goo, M. Prokop, C. Jacobs, B. van Ginneken and C. Schaefer-Prokop, "Visual Discrimination of Screen-detected Persistent from Transient Subsolid Nodules: an Observer Study", PLoS One, 2018;13(2):e0191874.
    Abstract DOI PMID Cited by ~4
  22. J. Charbonnier, K. Chung, E. Scholten, E. van Rikxoort, C. Jacobs, N. Sverzellati, M. Silva, U. Pastorino, B. van Ginneken and F. Ciompi, "Automatic segmentation of the solid core and enclosed vessels in subsolid pulmonary nodules", Nature Scientific Reports, 2018;8(1):646.
    Abstract DOI PMID Cited by ~9
  23. S. van Riel, F. Ciompi, M. Winkler Wille, A. Dirksen, S. Lam, E. Scholten, S. Rossi, N. Sverzellati, M. Naqibullah, R. Wittenberg, M. Hovinga-de Boer, M. Snoeren, L. Peters-Bax, O. Mets, M. Brink, M. Prokop, C. Schaefer-Prokop and B. van Ginneken, "Malignancy risk estimation of pulmonary nodules in screening CTs: Comparison between a computer model and human observers", PLoS One, 2017;12(11):e0185032.
    Abstract DOI PMID Cited by ~18
  24. F. Ciompi, K. Chung, S. van Riel, A. Setio, P. Gerke, C. Jacobs, E. Scholten, C. Schaefer-Prokop, M. Wille, A. Marchiano, U. Pastorino, M. Prokop and B. van Ginneken, "Towards automatic pulmonary nodule management in lung cancer screening with deep learning", Nature Scientific Reports, 2017(46479).
    Abstract DOI PMID arXiv Cited by ~171
  25. G. Litjens, T. Kooi, B. Ehteshami Bejnordi, A. Setio, F. Ciompi, M. Ghafoorian, J. van der Laak, B. van Ginneken and C. Sánchez, "A Survey on Deep Learning in Medical Image Analysis", Medical Image Analysis, 2017;42:60-88.
    Abstract DOI PMID arXiv Cited by ~3681
  26. K. Chung, C. Jacobs, E. Scholten, J. Goo, H. Prosch, N. Sverzellati, F. Ciompi, O. Mets, P. Gerke, M. Prokop, B. van Ginneken and C. Schaefer-Prokop, "Lung-RADS Category 4X: Does It Improve Prediction of Malignancy in Subsolid Nodules?", Radiology, 2017;284(1):264-271.
    Abstract DOI PMID Cited by ~32
  27. S. van Riel, F. Ciompi, C. Jacobs, M. Winkler Wille, E. Scholten, M. Naqibullah, S. Lam, M. Prokop, C. Schaefer-Prokop and B. van Ginneken, "Malignancy risk estimation of screen-detected nodules at baseline CT: comparison of the PanCan model, Lung-RADS and NCCN guidelines", European Radiology, 2017;27(10):4019-4029.
    Abstract DOI PMID Cited by ~28
  28. J. Charbonnier, E. van Rikxoort, A. Setio, C. Schaefer-Prokop, B. van Ginneken and F. Ciompi, "Improving Airway Segmentation in Computed Tomography using Leak Detection with Convolutional Networks", Medical Image Analysis, 2017;36:52-60.
    Abstract DOI PMID Cited by ~49
  29. F. Ciompi, S. Balocco, J. Rigla, X. Carrillo, J. Mauri and P. Radeva, "Computer-aided detection of intracoronary stent in intravascular ultrasound sequences", Medical Physics, 2016;43(10):5616.
    Abstract DOI PMID Cited by ~7
  30. A. Setio, F. Ciompi, G. Litjens, P. Gerke, C. Jacobs, S. van Riel, M. Wille, M. Naqibullah, C. Sánchez and B. van Ginneken, "Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks", IEEE Transactions on Medical Imaging, 2016;35(5):1160-1169.
    Abstract DOI PMID Cited by ~605
  31. J. Charbonnier, M. Brink, F. Ciompi, E. Scholten, C. Schaefer-Prokop and E. van Rikxoort, "Automatic Pulmonary Artery-Vein Separation and Classification in Computed Tomography Using Tree Partitioning and Peripheral Vessel Matching", IEEE Transactions on Medical Imaging, 2016:882-892.
    Abstract DOI PMID Cited by ~21
  32. F. Ciompi, B. de Hoop, S. van Riel, K. Chung, E. Scholten, M. Oudkerk, P. de Jong, M. Prokop and B. van Ginneken, "Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box", Medical Image Analysis, 2015;26(1):195-202.
    Abstract DOI PMID Cited by ~191
  33. F. Ciompi, C. Jacobs, E. Scholten, M. Winkler Wille, P. de Jong, M. Prokop and B. van Ginneken, "Bag of frequencies: a descriptor of pulmonary nodules in Computed Tomography images", IEEE Transactions on Medical Imaging, 2015;34(4):1-12.
    Abstract DOI PMID Cited by ~41
  34. C. Gatta and F. Ciompi, "Stacked sequential scale-space Taylor context", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014;36(8):1694-1700.
    Abstract DOI PMID Cited by ~11
  35. S. Balocco, C. Gatta, F. Ciompi, A. Wahle, P. Radeva, S. Carlier, G. Unal, E. Sanidas, J. Mauri, X. Carillo, T. Kovarnik, C. Wang, H. Chen, T. Exarchos, D. Fotiadis, F. Destrempes, G. Cloutier, O. Pujol, M. Alberti, E. Mendizabal-Ruiz, M. Rivera, T. Aksoy, R. Downe and I. Kakadiaris, "Standardized evaluation methodology and reference database for evaluating IVUS image segmentation", Computerized Medical Imaging and Graphics, 2014;38:70-90.
    Abstract DOI PMID Cited by ~88
  36. F. Ciompi, O. Pujol and P. Radeva, "ECOC-DRF: Discriminative Random Fields based on Error-Correcting Output Codes", Pattern Recognition, 2014;47:2193-2204.
    Abstract DOI Cited by ~5
  37. F. Ciompi, O. Pujol, C. Gatta, M. Alberti, S. Balocco, X. Carrillo, J. Mauri-Ferre and P. Radeva, "HoliMAb: A holistic approach for Media--Adventitia border detection in intravascular ultrasound", Medical Image Analysis, 2012.
    Abstract PMID Url Cited by ~53
  38. M. Alberti, S. Balocco, C. Gatta, F. Ciompi, O. Pujol, J. Silva, X. Carrillo and P. Radeva, "Automatic bifurcation detection in coronary IVUS sequences", IEEE Transactions on Biomedical Engineering, 2012;59(4):1022-1031.
    Abstract DOI PMID Cited by ~42
  39. X. Carrillo, E. Fernandez-Nofrerias, F. Ciompi, O. Rodriguez-Leor, P. Radeva, N. Salvatella, O. Pujol, J. Mauri and A. Bayes-Genis, "Changes in radial artery volume assessed using intravascular ultrasound: a comparison of two vasodilator regimens in transradial coronary interventions", Journal of Invasive Cardiology, 2011;23(10):401-404.
    Abstract PMID Url Cited by ~20
  40. J. Seabra, F. Ciompi, O. Pujol, J. Mauri, P. Radeva and J. Sanches, "Rayleigh mixture model for plaque characterization in intravascular ultrasound", IEEE Transactions on Biomedical Engineering, 2011;58(5):1314-1324.
    Abstract Url Cited by ~80
  41. F. Ciompi, O. Pujol, C. Gatta, O. Rodriguez-Leor, J. Mauri-Ferre and P. Radeva, "Fusing in-vitro and in-vivo intravascular ultrasound data for plaque characterization", International Journal of Cardiac Imaging, 2010;26(7):763-779.
    Abstract PMID Url Cited by ~28

Papers in conference proceedings

  1. W. Aswolinskiy, D. Tellez, G. Raya, L. van der Woude, M. Looijen-Salamon, J. van der Laak, K. Grunberg and F. Ciompi, "Neural image compression for non-small cell lung cancer subtype classification in H&E stained whole-slide images", Medical Imaging 2021: Digital Pathology, 2021;11603:1 - 7.
    Abstract DOI
  2. J. Vermazeren, L. van Eekelen, L. Meesters, M. Looijen-Salamon, S. Vos, E. Munari, C. Mercan and F. Ciompi, "muPEN: Multi-class PseudoEdgeNet for PD-L1 assessment", Medical Imaging with Deep Learning, 2021.
    Abstract Url
  3. G. Smit, F. Ciompi, M. Cigéhn, A. Bodén, J. van der Laak and C. Mercan, "Quality control of whole-slide images through multi-class semantic segmentation of artifacts", Medical Imaging with Deep Learning, 2021.
    Abstract Url
  4. M. van Rijthoven, M. Balkenhol, M. Atzori, P. Bult, J. van der Laak and F. Ciompi, "Few-shot weakly supervised detection and retrieval in histopathology whole-slide images", Medical Imaging, 2021;11603:137 - 143.
    Abstract DOI
  5. D. Tellez, D. Hoppener, C. Verhoef, D. Grunhagen, P. Nierop, M. Drozdzal, J. van der Laak and F. Ciompi, "Extending Unsupervised Neural Image Compression With Supervised Multitask Learning", Medical Imaging with Deep Learning, 2020.
    Abstract
  6. C. Mercan, G. Reijnen-Mooij, D. Martin, J. Lotz, N. Weiss, M. van Gerven and F. Ciompi, "Virtual staining for mitosis detection in Breast Histopathology", IEEE International Symposium on Biomedical Imaging, 2020:1770-1774.
    Abstract DOI
  7. Z. Swiderska-Chadaj, E. Stoelinga, A. Gertych and F. Ciompi, "Multi-Patch Blending improves lung cancer growth pattern segmentation in whole-slide images", IEEE International Conference on Computational Problems of Electrical Engineering, 2020.
    Abstract DOI
  8. Z. Swiderska-Chadaj, K. Nurzynska, G. Bartlomiej, K. Grunberg, L. van der Woude, M. Looijen-Salamon, A. Walts, T. Markiewicz, F. Ciompi and A. Gertych, "A deep learning approach to assess the predominant tumor growth pattern in whole-slide images of lung adenocarcinoma", Medical Imaging, 2020;11320:113200D.
    Abstract DOI
  9. C. Mercan, M. Balkenhol, J. van der Laak and F. Ciompi, "From Point Annotations to Epithelial Cell Detection in Breast Cancer Histopathology using RetinaNet", Medical Imaging with Deep Learning, 2019.
    Abstract Url
  10. J. Bokhorst, H. Pinckaers, P. van Zwam, I. Nagetgaal, J. van der Laak and F. Ciompi, "Learning from sparsely annotated data for semantic segmentation in histopathology images", Medical Imaging with Deep Learning, 2019;102:81-94.
    Abstract Url Cited by ~2
  11. D. Tellez, M. Balkenhol, N. Karssemeijer, G. Litjens, J. van der Laak and F. Ciompi, "H&E stain augmentation improves generalization of convolutional networks for histopathological mitosis detection", Medical Imaging, 2018;10581.
    Abstract DOI
  12. Z. Swiderska-Chadaj, H. Pinckaers, M. van Rijthoven, M. Balkenhol, M. Melnikova, O. Geessink, Q. Manson, G. Litjens, J. van der Laak and F. Ciompi, "Convolutional Neural Networks for Lymphocyte detection in Immunohistochemically Stained Whole-Slide Images", Medical Imaging with Deep Learning, 2018.
    Abstract Url Cited by ~5
  13. J. Bokhorst, L. Rijstenberg, D. Goudkade, I. Nagtegaal, J. van der Laak and F. Ciompi, "Automatic Detection of Tumor Budding in Colorectal Carcinoma with Deep Learning", Computational Pathology and Ophthalmic Medical Image Analysis, 2018.
    Abstract DOI Cited by ~2
  14. D. Tellez, J. van der Laak and F. Ciompi, "Gigapixel Whole-Slide Image Classification Using Unsupervised Image Compression And Contrastive Training", Medical Imaging with Deep Learning, 2018.
    Abstract Url Cited by ~1
  15. M. van Rijthoven, Z. Swiderska-Chadaj, K. Seeliger, J. van der Laak and F. Ciompi, "You Only Look on Lymphocytes Once", Medical Imaging with Deep Learning, 2018.
    Abstract Url Cited by ~2
  16. P. Bándi, R. van de Loo, M. Intezar, D. Geijs, F. Ciompi, B. van Ginneken, J. van der Laak and G. Litjens, "Comparison of Different Methods for Tissue Segmentation In Histopathological Whole-Slide Images", IEEE International Symposium on Biomedical Imaging, 2017:591-595.
    Abstract DOI arXiv Cited by ~14
  17. F. Ciompi, O. Geessink, B. Bejnordi, G. de Souza, A. Baidoshvili, G. Litjens, B. van Ginneken, I. Nagtegaal and J. van der Laak, "The importance of stain normalization in colorectal tissue classification with convolutional networks", IEEE International Symposium on Biomedical Imaging, 2017:160-163.
    Abstract DOI arXiv Cited by ~75
  18. N. Lessmann, I. Išgum, A. Setio, B. de Vos, F. Ciompi, P. de Jong, M. Oudkerk, W. Mali, M. Viergever and B. van Ginneken, "Deep convolutional neural networks for automatic coronary calcium scoring in a screening study with low-dose chest CT", Medical Imaging, 2016;9785:978511-1 - 978511-6.
    Abstract DOI Cited by ~25
  19. A. Setio, C. Jacobs, F. Ciompi, S. van Riel, M. Wille, A. Dirksen, E. van Rikxoort and B. van Ginneken, "Computer-aided detection of lung cancer: combining pulmonary nodule detection systems with a tumor risk prediction model", Medical Imaging, 2015;9414(94141O).
    Abstract DOI Cited by ~2
  20. B. van Ginneken, A. Setio, C. Jacobs and F. Ciompi, "Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans", IEEE International Symposium on Biomedical Imaging, 2015:286-289.
    Abstract DOI Cited by ~227
  21. F. Ciompi, C. Jacobs, E. Scholten, S. van Riel, M. Wille, M. Prokop and B. van Ginneken, "Automatic detection of spiculation of pulmonary nodules in Computed Tomography images", Medical Imaging, 2015;9414(941409).
    Abstract DOI Cited by ~6
  22. F. Ciompi, S. Balocco, C. Caus, J. Mauri and P. Radeva, "Stent Shape Estimation through a Comprehensive Interpretation of Intravascular Ultrasound Images", Medical Image Computing and Computer-Assisted Intervention, 2013:345-352.
    Abstract Cited by ~6
  23. F. Ciompi, R. Hua, S. Balocco, M. Alberti, O. Pujol, C. Caus, J. Mauri and P. Radeva, "Learning to Detect Stent Struts in Intravascular Ultrasound", Pattern Recognition and Image Analysis, 2013:575-583.
    Abstract Url Cited by ~4
  24. M. Alberti, C. Gatta, S. Balocco, F. Ciompi, O. Pujol, J. Silva, X. Carrillo and P. Radeva, "Automatic branching detection in IVUS sequences", Pattern Recognition and Image Analysis, 2011:126-133.
    Abstract Url Cited by ~4
  25. F. Ciompi, O. Pujol, C. Gatta, X. Carrillo, J. Mauri and P. Radeva, "A holistic approach for the detection of media-adventitia border in IVUS", Medical Image Computing and Computer-Assisted Intervention, 2011:411-419.
    Abstract Url Cited by ~11
  26. S. Balocco, C. Gatta, F. Ciompi, O. Pujol, X. Carrillo, J. Mauri and P. Radeva, "Combining Growcut and temporal correlation for IVUS lumen segmentation", Pattern Recognition and Image Analysis, 2011:556-563.
    Abstract Url Cited by ~25
  27. J. Seabra, J. Sanches, F. Ciompi and P. Radeva, "Ultrasonographic plaque characterization using a rayleigh mixture model", IEEE International Symposium on Biomedical Imaging, 2010:1-4.
    Abstract Url Cited by ~11
  28. F. Ciompi, O. Pujol and P. Radeva, "A meta-learning approach to conditional random fields using error-correcting output codes", International Conference on Pattern Recognition, 2010:710-713.
    Abstract Url Cited by ~5
  29. C. Gatta, S. Balocco, F. Ciompi, R. Hemetsberger, O. Leor and P. Radeva, "Real-time gating of IVUS sequences based on motion blur analysis: method and quantitative validation", Medical Image Computing and Computer-Assisted Intervention, 2010:59-67.
    Abstract DOI Cited by ~27
  30. C. Gatta, J. Valencia, F. Ciompi, O. Leor and P. Radeva, "Toward robust myocardial blush grade estimation in contrast angiography", Pattern Recognition and Image Analysis, 2009:249-256.
    Abstract Url Cited by ~2
  31. F. Ciompi, O. Pujol, O. Leor, C. Gatta, A. Vida and P. Radeva, "Enhancing in-vitro IVUS data for tissue characterization", Pattern Recognition and Image Analysis, 2009:241-248.
    Abstract Url Cited by ~6
  32. F. Ciompi, O. Pujol, E. Fernandez-Nofrerias, J. Mauri and P. Radeva, "Ecoc random fields for lumen segmentation in radial artery ivus sequences", Medical Image Computing and Computer-Assisted Intervention, 2009:869-876.
    Abstract Url Cited by ~24

Abstracts

  1. J. Bokhorst, F. Ciompi, I. Zlobec, A. Lugli, M. Vieth, R. Kirsch, J. van der Laak and I. Nagtegaal, "Computer-assisted hot-spot selection for tumor budding assessment in colorectal cancer", European Congress of Pathology, 2020.
    Abstract
  2. C. Mercan, M. Balkenhol, J. Laak and F. Ciompi, "Grading nuclear pleomorphism in breast cancer using deep learning", European Congress of Pathology, 2020.
    Abstract
  3. J. Bokhorst, I. Nagtegaal, I. Zlobec, A. Lugli, M. Vieth, R. Kirsch, J. van der Laak and F. Ciompi, "Deep learning based tumor bud detection in pan-cytokeratin stained colorectal cancer whole-slide images", European Congress of Pathology, 2020.
    Abstract
  4. J. Bokhorst, H. Dawson, A. Blank, I. Zlobec, A. Lugli, M. Vieth, R. Kirsch, M. Urbanowicz, S. Brockmoeller, J. Flejou, L. Rijstenberg, J. van der Laak, F. Ciompi and I. Nagtegaal, "Assessment of tumor buds in colorectal cancer. A large-scale international digital observer study", European Congress of Pathology, 2019.
    Abstract
  5. E. Smeets, J. Teuwen, J. van der Laak, M. Gotthardt, F. Ciompi and E. Aarntzen, "Tumor heterogeneity as a PET-biomarker predicts overall survival of pancreatic cancer patients", European Society for Molecular Imaging, 2018.
    Abstract
  6. M. Silva, G. Capretti, N. Sverzellati, C. Jacobs, F. Ciompi, B. van Ginneken, C. Schaefer-Prokop, M. Prokop, A. Marchiano and U. Pastorino, "Non-solid and Part-solid Nodules: Comparison Between Visual and Computer Aided Detection", World Congress of Thoracic Imaging, 2017.
    Abstract
  7. M. Silva, G. Capretti, N. Sverzellati, C. Jacobs, F. Ciompi, B. van Ginneken, C. Schaefer-Prokop, A. Marchianò and U. Pastorino, "Subsolid and part-solid nodules in lung cancer screening: comparison between visual and computer-aided detection", European Congress of Radiology, 2017.
    Abstract
  8. F. Ciompi, K. Chung, A. Setio, S. van Riel, E. Scholten, P. Gerke, C. Jacobs, U. Pastorino, A. Marchiano, M. Wille, M. Prokop and B. van Ginneken, "Pulmonary nodule type classification with convolutional networks", Medical Image Computing and Computer-Assisted Intervention, 2016.
    Abstract
  9. J. Charbonnier, M. Brink, F. Ciompi, E. Scholten, C. Schaefer-Prokop and E. Van Rikxoort, "Automatic Separation and Classification of Arteries and Veins in Non-Contrast Thoracic CT Scans", Annual Meeting of the Radiological Society of North America, 2015.
    Abstract Url
  10. S. van Riel, F. Ciompi, M. Wille, M. Naqibullah, E. Scholten, C. Schaefer-Prokop and B. van Ginneken, "Lung-RADS versus the McWilliams nodule malignancy score for risk prediction: an evaluation using lesions from the DLCST Trial", World Conference on Lung Cancer, 2015.
    Abstract
  11. S. van Riel, F. Ciompi, M. Wille, E. Scholten, A. Dirksen, K. Chung, M. Prokop, C. Schaefer-Prokop and B. van Ginneken, "Comparing LungRADS and the McWilliams nodule malignancy score: which approach works best to select screen detected pulmonary nodules for more aggressive followup?", Annual Meeting of the Radiological Society of North America, 2015.
    Abstract
  12. K. Chung, E. Scholten, S. van Riel, F. Ciompi, P. de Jong, M. Wille, M. Prokop, B. van Ginneken and C. Schaefer-Prokop, "Differentiation of persistent and transient subsolid nodules: does morphology help?", European Congress of Radiology, 2015;85(3):648-652.
    Abstract
  13. S. van Riel, F. Ciompi, M. Wille, E. Scholten, N. Sverzellati, S. Rossi, A. Dirksen, M. Brink, R. Wittenberg, M. Naqibullah, M. Prokop, C. Schaefer-Prokop and B. van Ginneken, "Can morphological features differentiate between malignant and benign pulmonary nodules, detected in a screen setting?", Annual Meeting of the Radiological Society of North America, 2015.
    Abstract
  14. F. Ciompi, B. de Hoop, C. Jacobs, M. Prokop, P. a de Jong and B. van Ginneken, "Automatic Classification of Perifissural Pulmonary Nodules in Thoracic CT Images", Annual Meeting of the Radiological Society of North America, 2014.
    Abstract

PhD theses

  1. M. Balkenhol, "Tissue-based biomarker assessment for predicting prognosis of triple negative breast cancer: the additional value of artificial intelligence", 2020.
    Abstract Url
  2. J. Charbonnier, "Segmentation & quantification of airways and blood vessels in chest CT", 2017.
    Abstract Url
  3. F. Ciompi, "Multi-Class Learning for Vessel Characterization in Intravascular Ultrasound", 2012.
    Abstract Url