Publications

2020

Papers in international journals

  1. G. Humpire Mamani, J. Bukala, E. Scholten, M. Prokop, B. van Ginneken and C. Jacobs, "Fully Automatic Volume Measurement of the Spleen at CT Using Deep Learning", Radiology: Artificial Intelligence, 2020;2(4):e190102.
    Abstract DOI PMID Cited by ~1 Algorithm
  2. A. Schreuder, C. Jacobs, E. Scholten, B. van Ginneken, C. Schaefer-Prokop and M. Prokop, "Typical CT Features of Intrapulmonary Lymph Nodes: A Review", Radiology: Cardiothoracic Imaging, 2020;2(4):e190159.
    Abstract DOI PMID Download
  3. A. Rossi, M. Hosseinzadeh, M. Bianchini, F. Scarselli and H. Huisman, "Multi-modal siamese network for diagnostically similar lesion retrieval in prostate MRI", IEEE Transactions on Medical Imaging, 2020.
    Abstract DOI PMID
  4. B. van Ginneken, "The Potential of Artificial Intelligence to Analyze Chest Radiographs for Signs of COVID-19 Pneumonia", Radiology, 2020:204238.
    Abstract DOI PMID
  5. A. Meyer, G. Chlebus, M. Rak, D. Schindele, M. Schostak, B. van Ginneken, A. Schenk, H. Meine, H. Hahn, A. Schreiber and C. Hansen, "Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI", Computer Methods and Programs in Biomedicine, 2020:105821.
    Abstract DOI PMID arXiv
  6. M. Silva, G. Milanese, S. Sestini, F. Sabia, C. Jacobs, B. van Ginneken, M. Prokop, C. Schaefer-Prokop, A. Marchiano, N. Sverzellati and U. Pastorino, "Lung cancer screening by nodule volume in Lung-RADS v1.1: negative baseline CT yields potential for increased screening interval", European Radiology, 2020;31(4):1956-1968.
    Abstract DOI PMID
  7. M. Hermsen, B. Smeets, L. Hilbrands and J. van der Laak, "Artificial intelligence; is there a potential role in nephropathology?", Nephrology Dialysis Transplantation, 2020.
    Abstract DOI PMID
  8. Z. Swiderska-Chadaj, K. Hebeda, M. van den Brand and G. Litjens, "Artificial intelligence to detect MYC translocation in slides of diffuse large B-cell lymphoma", Virchows Archiv, 2020.
    Abstract DOI PMID
  9. M. Meijs, F. Meijer, M. Prokop, B. van Ginneken and R. Manniesing, "Image-level detection of arterial occlusions in 4D-CTA of acute stroke patients using deep learning", Medical Image Analysis, 2020;66:101810.
    Abstract DOI PMID
  10. L. Maier-Hein, A. Reinke, M. Kozubek, A. L. Martel, T. Arbel, M. Eisenmann, A. Hanbuary, P. Jannin, H. Muller, S. Onogur, J. Saez-Rodriguez, B. van Ginneken, A. Kopp-Schneider and B. Landman, "BIAS: Transparent reporting of biomedical image analysis challenges", Medical Image Analysis, 2020;66:101796.
    Abstract DOI PMID arXiv Cited by ~5
  11. T. Kootstra, J. Teuwen, J. Goudsmit, T. Nijboer, M. Dodd and S. Van der Stigchel, "Machine learning-based classification of viewing behavior using a wide range of statistical oculomotor features", Journal of Vision, 2020;20(9):1.
    Abstract DOI PMID
  12. Z. Swiderska-Chadaj, T. de Bel, L. Blanchet, A. Baidoshvili, D. Vossen, J. van der Laak and G. Litjens, "Impact of rescanning and normalization on convolutional neural network performance in multi-center, whole-slide classification of prostate cancer", Scientific Reports, 2020;10(1):14398.
    Abstract DOI PMID Download
  13. H. Pinckaers, B. van Ginneken and G. Litjens, "Streaming convolutional neural networks for end-to-end learning with multi-megapixel images", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.
    Abstract DOI PMID arXiv
  14. S. Schalekamp, M. Huisman, R. van Dijk, M. Boomsma, P. Freire Jorge, W. de Boer, G. Herder, M. Bonarius, O. Groot, E. Jong, A. Schreuder and C. Schaefer-Prokop, "Model-based Prediction of Critical Illness in Hospitalized Patients with COVID-19", Radiology, 2020:202723.
    Abstract DOI PMID
  15. 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.
    Abstract DOI PMID
  16. C. González-Gonzalo, B. Liefers, B. van Ginneken and C. Sánchez, "Iterative augmentation of visual evidence for weakly-supervised lesion localization in deep interpretability frameworks: application to color fundus images", IEEE Transactions on Medical Imaging, 2020;39(11):3499-3511.
    Abstract DOI PMID
  17. W. Xie, C. Jacobs, J. Charbonnier and B. van Ginneken, "Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans", IEEE Transactions on Medical Imaging, 2020;39(8):2664-2675.
    Abstract DOI PMID Cited by ~5 Algorithm Download
  18. A. Schreuder, C. Jacobs, E. Scholten, M. Prokop, B. van Ginneken, D. Lynch and C. Schaefer-Prokop, "Association between the number and size of intrapulmonary lymph nodes and chronic obstructive pulmonary disease severity", PeerJ, 2020;8:e9166.
    Abstract DOI PMID Download
  19. I. Sechopoulos, J. Teuwen and R. Mann, "Artificial Intelligence for Breast Cancer Detection in Mammography: state of the art", Seminars in Cancer Biology, 2020.
    Abstract DOI PMID
  20. G. van Leenders, T. van der Kwast, D. Grignon, A. Evans, G. Kristiansen, C. Kweldam, G. Litjens, J. McKenney, J. Melamed, N. Mottet, G. Paner, H. Samaratunga, I. Schoots, J. Simko, T. Tsuzuki, M. Varma, A. Warren, T. Wheeler, S. Williamson, K. Iczkowski and I. Members, "The 2019 International Society of Urological Pathology (ISUP) Consensus Conference on Grading of Prostatic Carcinoma.", American Journal of Surgical Pathology, 2020;44(8):e87-e99.
    Abstract DOI PMID
  21. 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 Download
  22. 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 Download
  23. K. Murphy, H. Smits, A. Knoops, M. Korst, T. Samson, E. Scholten, S. Schalekamp, C. Schaefer-Prokop, R. Philipsen, A. Meijers, J. Melendez, B. van Ginneken and M. Rutten, "COVID-19 on the Chest Radiograph: A Multi-Reader Evaluation of an AI System", Radiology, 2020;296:E166-E172.
    Abstract DOI PMID Cited by ~12
  24. M. Prokop, W. van Everdingen, T. van Rees Vellinga, J. van Quarles Ufford, L. Stoger, L. Beenen, B. Geurts, H. Gietema, J. Krdzalic, C. Schaefer-Prokop, B. van Ginneken, M. Brink and the COVID-19 Standardized Reporting Working Group of the Dutch Radiological Society, "CO-RADS - A categorical CT assessment scheme for patients with suspected COVID-19: definition and evaluation", Radiology, 2020;296(2):E97-E104.
    Abstract DOI PMID Cited by ~47
  25. S. Habib, S. Rafiq, S. Zaidi, R. Ferrand, J. Creswell, B. Van Ginneken, W. Jamal, K. Azeemi, S. Khowaja and A. Khan, "Evaluation of computer aided detection of tuberculosis on chest radiography among people with diabetes in Karachi Pakistan", Nature Scientific Reports, 2020;10(1):6276.
    Abstract DOI PMID
  26. K. Murphy, S. Habib, S. Zaidi, S. Khowaja, A. Khan, J. Melendez, E. Scholten, F. Amad, S. Schalekamp, M. Verhagen, R. Philipsen, A. Meijers and B. van Ginneken, "Computer aided detection of tuberculosis on chest radiographs: An evaluation of the CAD4TB v6 system", Nature Scientific Reports, 2020;10:5492.
    Abstract DOI PMID arXiv Cited by ~8
  27. C. van 't Klooster, H. Nathoe, J. Hjortnaes, M. Bots, I. Isgum, N. Lessmann, Y. van der Graaf, T. Leiner and F. Visseren, "Multifocal cardiovascular calcification in patients with established cardiovascular disease; prevalence, risk factors, and relation with recurrent cardiovascular disease", International Journal of Cardiology: Heart and Vasculature, 2020;27:100499.
    Abstract DOI PMID
  28. B. Liefers, J. Colijn, C. González-Gonzalo, T. Verzijden, J. Wang, N. Joachim, P. Mitchell, C. Hoyng, B. van Ginneken, C. Klaver and C. Sánchez, "A deep learning model for segmentation of geographic atrophy to study its long-term natural history", Ophthalmology, 2020;127(8):1086-1096.
    Abstract DOI PMID arXiv Cited by ~2
  29. 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.
    Abstract DOI PMID Download
  30. I. Olaciregui-Ruiz, I. Torres-Xirau, J. Teuwen, U. van der Heide and A. Mans, "A Deep Learning-based correction to EPID dosimetry for attenuation and scatter in the Unity MR-Linac system", Physica Medica, 2020;71:124-131.
    Abstract DOI PMID
  31. H. Kauczor, A. Baird, T. Blum, L. Bonomo, C. Bostantzoglou, O. Burghuber, B. Čepicka, A. Comanescu, S. Courad, A. Devaraj, V. Jespersen, S. Morozov, I. Agmon, N. Peled, P. Powell, H. Prosch, S. Ravara, J. Rawlinson, M. Revel, M. Silca, A. Snoeckx, B. van Ginneken, J. van Meerbeeck, C. Vardavas, O. von Stackelberg, M. Gaga, O. behalf of the of (ESR) and T. (ERS), "ESR/ERS statement paper on lung cancer screening", European Radiology, 2020;30:3277-3294.
    Abstract DOI PMID
  32. H. Kauczor, A. Baird, T. Blum, L. Bonomo, C. Bostantzoglou, O. Burghuber, B. Čepicka, A. Comanescu, S. Couraud, A. Devaraj, V. Jespersen, S. Morozov, I. Nardi Agmon, N. Peled, P. Powell, H. Prosch, S. Ravara, J. Rawlinson, M. Revel, M. Silva, A. Snoeckx, B. van Ginneken, J. van Meerbeeck, C. Vardavas, O. von Stackelberg, M. Gaga, E. of (ESR) and T. (ERS), "ESR/ERS statement paper on lung cancer screening", European Respiratory Journal, 2020;55(2):1900506.
    Abstract DOI PMID Cited by ~5
  33. M. Sieren, F. Brenne, A. Hering, H. Kienapfel, N. Gebauer, T. Oechtering, A. Fürschke, F. Wegner, E. Stahlberg, S. Heldmann, J. Barkhausen and A. Frydrychowicz, "Rapid study assessment in follow-up whole-body computed tomography in patients with multiple myeloma using a dedicated bone subtraction software", European Radiology, 2020;30:3198-3209.
    Abstract DOI PMID
  34. S. van Velzen, N. Lessmann, B. Velthuis, I. Bank, D. van den Bongard, T. Leiner, P. de Jong, W. Veldhuis, A. Correa, J. Terry, J. Carr, M. Viergever, H. Verkooijen and I. Išgum, "Deep learning for automatic calcium scoring in CT: Validation using multiple cardiac CT and chest CT protocols", Radiology, 2020;295(1):66-79.
    Abstract DOI PMID Cited by ~10
  35. H. de Koning, C. van der Aalst, P. de Jong, E. Scholten, K. Nackaerts, M. Heuvelmans, J. Lammers, C. Weenink, U. Yousaf-Khan, N. Horeweg, S. van 't Westeinde, M. Prokop, W. Mali, F. Mohamed Hoesein, P. van Ooijen, J. Aerts, M. den Bakker, E. Thunnissen, J. Verschakelen, R. Vliegenthart, J. Walter, K. Ten Haaf, H. Groen and M. Oudkerk, "Reduced Lung-Cancer Mortality with Volume CT Screening in a Randomized Trial", New England Journal of Medicine, 2020;382(6):503-513.
    Abstract DOI PMID Cited by ~105
  36. T. Boers, Y. Hu, E. Gibson, D. Barratt, E. Bonmati, J. Krdzalic, F. van der Heijden, J. Hermans and H. Huisman, "Interactive 3D U-net for the Segmentation of the Pancreas in Computed Tomography Scans", Physics in Medicine and Biology, 2020;65(6):065002.
    Abstract DOI PMID Download
  37. N. Mahomed, B. van Ginneken, R. Philipsen, J. Melendez, D. Moore, H. Moodley, T. Sewchuran, D. Mathew and S. Madhi, "Computer-aided diagnosis for World Health Organization-defined chest radiograph primary-endpoint pneumonia in children", Pediatric Radiology, 2020;50(4):482-491.
    Abstract DOI PMID Cited by ~3 Download
  38. W. Bulten, H. Pinckaers, H. van Boven, R. Vink, T. de Bel, B. van Ginneken, J. van der Laak, C. de Hulsbergen-van Kaa and G. Litjens, "Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study", Lancet Oncology, 2020;21(2):233-241.
    Abstract DOI PMID arXiv Cited by ~29 Algorithm Download
  39. F. Ayatollahi, S. Shokouhi and J. Teuwen, "Differentiating Benign and Malignant Mass and non-Mass Lesions in Breast DCE-MRI using Normalized Frequency-based Features", International Journal of Computer Assisted Radiology and Surgery, 2020;15(2):297-307.
    Abstract DOI PMID
  40. C. González-Gonzalo, V. Sánchez-Gutiérrez, P. Hernández-Martínez, I. Contreras, Y. Lechanteur, A. Domanian, B. van Ginneken and C. Sánchez, "Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age-related macular degeneration", Acta Ophthalmologica, 2020;98(4):368-377.
    Abstract DOI PMID Cited by ~9
  41. M. Dekker, F. Waissi, I. Bank, N. Lessmann, I. Išgum, B. Velthuis, A. Scholtens, G. Leenders, G. Pasterkamp, D. de Kleijn, L. Timmers and A. Mosterd, "Automated calcium scores collected during myocardial perfusion imaging improve identification of obstructive coronary artery disease", International Journal of Cardiology: Heart and Vasculature, 2020;26:100434.
    Abstract DOI PMID
  42. C. Celeng, R. Takx, N. Lessmann, P. Maurovich-Horvat, T. Leiner, I. Išgum and P. de Jong, "The association between marital status, coronary computed tomography imaging biomarkers, and mortality in a lung cancer screening population", Journal of Thoracic Imaging, 2020;35:204-209.
    Abstract DOI PMID
  43. S. van de Leemput, M. Prokop, B. van Ginneken and R. Manniesing, "Stacked Bidirectional Convolutional LSTMs for Deriving 3D Non-contrast CT from Spatiotemporal 4D CT", IEEE Transactions on Medical Imaging, 2020;39(4):985-996.
    Abstract DOI PMID Cited by ~4 Download
  44. J. van Zelst, T. Tan, R. Mann and N. Karssemeijer, "Validation of radiologists' findings by computer-aided detection (CAD) software in breast cancer detection with automated 3D breast ultrasound: a concept study in implementation of artificial intelligence software", Acta Radiologica, 2020;61(3):312-320.
    Abstract DOI PMID
  45. E. Sogancioglu, K. Murphy, E. Calli, E. Scholten, S. Schalekamp and B. Van Ginneken, "Cardiomegaly Detection on Chest Radiographs: Segmentation Versus Classification", IEEE Access, 2020;8:94631-94642.
    Abstract DOI
  46. J. Goudsmit and J. Teuwen, "Tussen data en theorie", Tijdschrift voor Toezicht, 2020;11(1):48-53.
    Abstract DOI
  47. A. Schreuder and C. Schaefer-Prokop, "Perifissural nodules: ready for application into lung cancer CT screening?", Annals of Translational Medicine, 2020.
    Abstract DOI
  48. E. Thee, D. Luttikhuizen, H. Lemij, F. Verbraak, C. Sánchez and C. Klaver, "Artificial intelligence for eye care", Nederlands Tijdschrift voor Geneeskunde, 2020.
    Abstract Url
  49. S. Kazeminia, C. Baur, A. Kuijper, B. van Ginneken, N. Navab, S. Albarqouni and A. Mukhopadhyay, "GANs for Medical Image Analysis", Artificial Intelligence in Medicine, 2020;109:101938.
    Abstract DOI arXiv Cited by ~55
  50. M. Meijs, S. Pegge, M. Vos, A. Patel, S. van de Leemput, K. Koschmieder, M. Prokop, F. Meijer and R. Manniesing, "Cerebral Artery and Vein Segmentation in Fourdimensional CT Angiography Using Convolutional Neural Networks", Radiology: Artificial Intelligence, 2020;2(4):e190178.
    Abstract DOI

Preprints

  1. N. Lessmann and B. van Ginneken, "Random smooth gray value transformations for cross modality learning with gray value invariant networks", arXiv:2003.06158, 2020.
    Abstract arXiv
  2. A. Meyer, G. Chlebus, M. Rak, D. Schindele, M. Schostak, B. van Ginneken, A. Schenk, H. Meine, H. Hahn, A. Schreiber and C. Hansen, "Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI", arXiv:2009.11120, 2020.
    Abstract arXiv
  3. S. Zhou, H. Greenspan, C. Davatzikos, J. Duncan, B. van Ginneken, A. Madabhushi, J. Prince, D. Rueckert and R. Summers, "A review of deep learning in medical imaging: Image traits, technology trends, case studies with progress highlights, and future promises", arXiv:2008.09104, 2020.
    Abstract arXiv
  4. P. Muller, B. Liefers, T. Treis, F. Gomes Rodrigues, A. Olvera-Barrios, B. Paul, N. Dhingra, A. Lotery, C. Bailey, P. Taylor, C. Sánchez and A. Tufail, "Reliability of retinal pathology quantification in age-related macular degeneration: Implications for clinical trials and machine learning applications", medrxiv, 2020.
    Abstract DOI

Papers in conference proceedings

  1. N. Moriakov, J. Adler and J. Teuwen, "Kernel of CycleGAN as a principal homogeneous space", International Conference on Learning Representations, 2020.
    Abstract Url
  2. Z. Swiderska-Chadaj, K. Hebeda, M. van den Brand and G. Litjens, "Predicting MYC translocation in HE specimens of diffuse large B-cell lymphoma through deep learning", Medical Imaging, 2020;11320:1132010.
    Abstract DOI
  3. A. Saha, F. Tushar, K. Faryna, V. D'Anniballe, R. Hou, M. Mazurowski, G. Rubin and J. Lo, "Weakly Supervised 3D Classification of Chest CT using Aggregated Multi-Resolution Deep Segmentation Features", Medical Imaging, 2020;11314:39 - 44.
    Abstract DOI arXiv
  4. 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
  5. J. Linmans, J. van der Laak and G. Litjens, "Efficient Out-of-Distribution Detection in Digital Pathology Using Multi-Head Convolutional Neural Networks", Medical Imaging with Deep Learning, 2020:465-478.
    Abstract Url
  6. K. Faryna, F. Tushar, V. D'Anniballe, R. Hou, G. Rubin and J. Lo, "Attention-guided classification of abnormalities in semi-structured computed tomography reports", Medical Imaging, 2020;11314:397 - 403.
    Abstract DOI
  7. A. Saha, M. Hosseinzadeh and H. Huisman, "Encoding Clinical Priori in 3D Convolutional Neural Networks for Prostate Cancer Detection in bpMRI", Medical Imaging Meets NeurIPS Workshop - 34th Conference on Neural Information Processing Systems (NeurIPS), 2020.
    Abstract arXiv
  8. L. van Eekelen, H. Pinckaers, K. Hebeda and G. Litjens, "Multi-class semantic cell segmentation and classification of aplasia in bone marrow histology images", Medical Imaging, 2020;11320:113200B.
    Abstract DOI
  9. 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
  10. 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
  11. K. Michielsen, N. Moriakov, J. Teuwen and I. Sechopoulos, "Deep Learning-based Initialization of Iterative Reconstruction for Breast Tomosynthesis", 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020.
    Abstract
  12. B. Lassen-Schmidt, A. Hering, S. Krass and H. Meine, "Automatic segmentation of the pulmonary lobes with a 3D u-net and optimized loss function", Medical Imaging with Deep Learning, 2020.
    Abstract Url
  13. K. Faryna, K. Koschmieder, M. Paul, T. van den Heuvel, A. van der Eerden, R. Manniesing and B. van Ginneken, "Adversarial cycle-consistent synthesis of cerebral microbleeds for data augmentation", Medical Imaging Meets NeurIPS Workshop - 34th Conference on Neural Information Processing Systems (NeurIPS), 2020.
    Abstract arXiv
  14. 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
  15. H. Altun, G. Chlebus, C. Jacobs, H. Meine, B. van Ginneken and H. Hahn, "Feasibility of End-To-End Trainable Two-Stage U-Net for Detection of Axillary Lymph Nodes in Contrast-Enhanced CT Based Scans on Sparse Annotations", Medical Imaging, 2020:113141C.
    Abstract DOI
  16. A. Saha, P. Prasad and A. Thabit, "Leveraging Adaptive Color Augmentation in Convolutional Neural Networks for Deep Skin Lesion Segmentation", IEEE International Symposium on Biomedical Imaging, 2020:2014-2017.
    Abstract DOI arXiv
  17. X. Yu, B. Lou, B. Shi, D. Winkel, N. Arrahmane, M. Diallo, T. Meng, H. von Busch, R. Grimm, B. Kiefer, D. Comaniciu, A. Kamen, H. Huisman, A. Rosenkrantz, T. Penzkofer, I. Shabunin, M. Choi, Q. Yang and D. Szolar, "False Positive Reduction Using Multiscale Contextual Features for Prostate Cancer Detection in Multi-Parametric MRI Scans", IEEE International Symposium on Biomedical Imaging, 2020.
    Abstract DOI
  18. A. Hering and S. Heldmann, "mlVIRNET: Improved Deep Learning Registration Using a Coarse to Fine Approach to Capture all Levels of Motion", Bildverarbeitung für die Medizin, 2020:175.
    Abstract DOI

Abstracts

  1. 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
  2. A. Ardu, B. Liefers, C. de Vente, C. González-Gonzalo, C. Klaver and C. Sánchez, "Artificial Intelligence for the Classification and Quantification of Reticular Pseudodrusen in Multimodal Retinal Images", European Society of Retina Specialists, 2020.
    Abstract Url
  3. C. González-Gonzalo, S. Wetstein, G. Bortsova, B. Liefers, B. van Ginneken and C. Sánchez, "Are adversarial attacks an actual threat for deep learning systems in real-world eye disease screening settings?", European Society of Retina Specialists, 2020.
    Abstract Url
  4. C. de Vente, M. van Grinsven, S. De Zanet, A. Mosinska, R. Sznitman, C. Klaver and C. Sánchez, "Estimating Uncertainty of Deep Neural Networks for Age-related Macular Degeneration Grading using Optical Coherence Tomography", Association for Research in Vision and Ophthalmology, 2020.
    Abstract
  5. 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.
    Abstract
  6. D. Grob, S. Schalekamp, L. Oostveen, W. van der Woude, C. Jacobs, M. Prokop, I. Sechopoulos and M. Brink, "Pulmonary nodule growth: can follow-up be shortened with a high-end or an ultra-high-resolution CT scanner?", European Congress of Radiology, 2020.
    Abstract
  7. 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
  8. 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.
    Abstract
  9. T. Riepe, M. Hosseinzadeh, P. Brand and H. Huisman, "Anisotropic Deep Learning Multi-planar Automatic Prostate Segmentation", Annual Meeting of the International Society for Magnetic Resonance in Medicine, 2020.
    Abstract
  10. W. Sanderink, J. Teuwen, L. Appelman, I. Sechopoulos, N. Karssemeijer and R. Mann, "Simultaneous multi-slice single-shot DWI compared to routine read-out-segmented DWI for evaluation of breast lesions", ISMRM Benelux, 2020.
    Abstract
  11. K. Venkadesh, A. Setio, Z. Saghir, B. van Ginneken and C. Jacobs, "Deep Learning for Lung Nodule Malignancy Prediction: Comparison With Clinicians and the Brock Model on an Independent Dataset From a Large Lung Screening Trial", Annual Meeting of the Radiological Society of North America, 2020.
    Abstract
  12. B. Liefers, P. Taylor, C. González-Gonzalo, A. Tufail and C. Sánchez, "Achieving expert level performance in quantifying 13 distinctive features of neovascular age-related macular degeneration on optical coherence tomography", European Society of Retina Specialists, 2020.
    Abstract Url
  13. T. Haddad, J. Bokhorst, L. van den Dobbelsteen, F. Simmer, J. van der Laak and I. Nagtegaal, "Characterisation of the tumour-host interface as a prognostic factor through deep learning systems", United European Gastroenterology Journal, 2020.
    Abstract
  14. K. van Leeuwen, S. Schalekamp, M. Rutten, B. van Ginneken and M. de Rooij, "Scientific Evidence for 100 Commercially Available Artificial Intelligence Tools for Radiology: A Systematic Review", Annual Meeting of the Radiological Society of North America, 2020.
    Abstract
  15. C. Mercan, M. Balkenhol, J. Laak and F. Ciompi, "Grading nuclear pleomorphism in breast cancer using deep learning", European Congress of Pathology, 2020.
    Abstract

PhD theses

  1. M. Balkenhol, "Tissue-based biomarker assessment for predicting prognosis of triple negative breast cancer: the additional value of artificial intelligence", PhD thesis, 2020.
    Abstract Url
  2. M. Meijs, "Automated Image Analysis and Machine Learning to Detect Cerebral Vascular Pathology in 4D-CTA", PhD thesis, 2020.
    Abstract Url
  3. S. van Riel, "Malignancy risk estimation of screen-detected pulmonary nodules", PhD thesis, 2020.
    Abstract Url

Master theses

  1. T. Payer, "AI-assisted PD-L1 scoring in non-small-cell lung cancer", Master thesis, 2020.
    Abstract
  2. L. van Eekelen, "Deep learning-based analysis of bone marrow histopathology images", Master thesis, 2020.
    Abstract
  3. T. van den Hout, "Automatic muscle and fat segmentation in 3D abdominal CT images for body composition assessment", Master thesis, 2020.
    Abstract
  4. M. Schilpzand, "Automatic Placenta Localisation from Ultrasound Imaging in a Resource-Limited Setting", Master thesis, 2020.
    Abstract
  5. T. de Boer, "A feasibility study for Deep Learning Image Guided Guidewire Tracking for Image-guided Interventions", Master thesis, 2020.
    Abstract
  6. J. Spronck, "Multi conditional lung nodule synthesis for improved nodule malignancy classification in Computed Tomography scans", Master thesis, 2020.
    Abstract Url
  7. K. Faryna, "Brain MRI synthesis via pathology factorization and adversarial cycle-consistent learning for data augmentation", Master thesis, 2020.
    Abstract
  8. I. Slootweg, "Patient variables related to false predictions of deep-learning assisted prostate cancer detection in MRI", Master thesis, 2020.
    Abstract
  9. R. Kluge, "Combining AI with Radiologists: exploring the possibilities in implementation of Computer-Aided Detection", Master thesis, 2020.
    Abstract
  10. A. Saha, M. Hosseinzadeh and H. Huisman, "Computer-Aided Detection of Clinically Significant Prostate Cancer in mpMRI", Master thesis, 2020.
    Abstract Url

Other publications

  1. J. Petersen, R. Estepar, A. Schmidt-Richberg, S. Gerard, B. Lassen-Schmidt, C. Jacobs, R. Beichel and K. Mori, "Thoracic Image Analysis", 2020;12502.
    Abstract DOI