Publications
2020
Papers in international journals
- 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.
- 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.
- P. den Exter, L. Kroft, C. Gonsalves, G. Le Gal, C. Schaefer-Prokop, M. Carrier, M. Huisman and F. Klok, "Establishing diagnostic criteria and treatment of subsegmental pulmonary embolism: A Delphi analysis of experts", Research and Practice in Thrombosis and Haemostasis, 2020;4:1251-1261.
- 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.
- B. van Ginneken, "The Potential of Artificial Intelligence to Analyze Chest Radiographs for Signs of COVID-19 Pneumonia", Radiology, 2020:204238.
- 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.
- 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.
- M. Hermsen, B. Smeets, L. Hilbrands and J. van der Laak, "Artificial intelligence; is there a potential role in nephropathology?", Nephrology Dialysis Transplantation, 2020.
- T. Hebar, Z. Snoj, L. Sconfienza, F. Vanhoenacker, M. Shahabpour, V. Salapura, A. Isaac, E. Drakonaki, Y. Vasilev, J. Drape, M. Adriaensen, K. Friedrich, G. Guglielmi, A. Vieira, H. Sanal, L. Kerttula, J. Hellund, J. Nagy, A. Heuck, M. Rutten, M. Tzalonikou, U. Hansen, J. Niemunis-Sawicka, F. Becce, E. Silvestri, E. Juan and K. Wörtler, "Present Status of Musculoskeletal Radiology in Europe: International Survey by the European Society of Musculoskeletal Radiology", Seminars in Musculoskeletal Radiology, 2020;24:323-330.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- W. Sanderink, L. Strobbe, P. Bult, M. Schlooz-Vries, S. Lardenoije, D. Venderink, I. Sechopoulos, N. Karssemeijer, W. Vreuls and R. Mann, "Minimally invasive breast cancer excision using the breast lesion excision system under ultrasound guidance", Breast Cancer Research and Treatment, 2020;184:37-43.
- 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.
- 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.
- J. Terheyden, F. Holz, S. Schmitz-Valckenberg, A. Lüning, M. Schmid, G. Rubin, H. Dunbar, A. Tufail, D. Crabb, A. Binns, C. Sánchez, C. Hoyng, P. Margaron, N. Zakaria, M. Durbin, U. Luhmann, P. Zamiri, J. Cunha-Vaz, C. Martinho, S. Leal, R. Finger, P. Basile, C. Behning, M. Berger, A. Binns, M. Böttger, C. Bouchet, J. Brazier, T. Butt, C. Carapezzi, J. Carlton, A. Charil, R. Coimbra, S. Nunes, D. Crabb, J. Cunha-Vaz, H. Dunbar, M. Durbin, R. Finger, F. Holz, C. Hoyng, J. Krätzschmar, S. Leal, U. Luhmann, A. Lüning, P. Margaron, C. Martinho, B. Melício, S. Mohand-Saïd, D. Rowen, G. Rubin, J. Sahel, C. Sánchez, D. Sanches Fernandes, M. Schmid, S. Schmitz-Valckenberg, A. Skelly, L. Stöhr, D. Taylor, J. Terheyden, A. Tufail, L. Vieweg, L. Wintergerst, C. Wojek, N. Zakaria, P. Zamiri and O. behalf of the consortium, "Clinical study protocol for a low-interventional study in intermediate age-related macular degeneration developing novel clinical endpoints for interventional clinical trials with a regulatory and patient access intention--MACUSTAR", Trials, 2020;21.
- W. Sanderink, M. Caballo, L. Strobbe, P. Bult, W. Vreuls, D. Venderink, I. Sechopoulos, N. Karssemeijer and R. Mann, "Reliability of MRI tumor size measurements for minimal invasive treatment selection in small breast cancers", European Journal of Surgical Oncology, 2020;46:1463-1470.
- I. Sechopoulos, J. Teuwen and R. Mann, "Artificial Intelligence for Breast Cancer Detection in Mammography: state of the art", Seminars in Cancer Biology, 2020.
- M. Omar, M. Roobol, M. Ribal, T. Abbott, P. Agapow, S. Araujo, A. Asiimwe, C. Auffray, I. Balaur, K. Beyer, C. Bernini, A. Bjartell, A. Briganti, J. Butler-Ransohoff, R. Campi, M. Cavelaars, B. De Meulder, Z. Devecseri, M. Voss, K. Dimitropoulos, S. Evans-Axelsson, B. Franks, L. Fullwood, D. Horgan, E. Smith, A. Kiran, K. Kivinummi, M. Lambrecht, D. Lancet, P. Lindgren, S. MacLennan, S. MacLennan, M. Nogueira, F. Moen, M. Moinat, K. Papineni, C. Reich, K. Reiche, S. Rogiers, C. Sartini, K. van Bochove, F. van Diggelen, M. Van Hemelrijck, H. Van Poppel, J. Zong, J. N'Dow, E. Andersson, H. Arala, A. Auvinen, C. Bangma, D. Burke, A. Cardone, J. Casariego, G. Cuperus, S. Dabestani, F. Esperto, N. Fossati, A. Fridhammar, G. Gandaglia, D. Tandefelt, F. Horn, J. Huber, J. Hugosson, H. Huisman, A. Josefsson, O. Kilkku, M. Kreuz, M. Lardas, J. Lawson, F. Lefresne, S. Lejeune, E. Longden-Chapman, G. McVie, L. Moris, N. Mottet, T. Murtola, C. Nicholls, K. Pang, K. Pascoe, M. Picozzi, K. Plass, P. Pohjanjousi, M. Reaney, S. Remmers, P. Robinson, J. Schalken, M. Schravendeel, T. Seisen, A. Servan, K. Shiranov, R. Snijder, C. Steinbeisser, N. Taibi, K. Talala, D. Tilki, T. den Van Broeck, Z. Vassilev, O. Voima, E. Vradi, R. Waldeck, W. Weistra, P. Willemse, M. Wirth, R. Wolfinger, N. Kermani and T. Consortium, "Introducing PIONEER: a project to harness big data in prostate cancer research", Nature Reviews Urology, 2020;17:351-362.
- 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.
- 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).
- 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.
- O. Hamer, B. Rehbock and C. Schaefer-Prokop, "Idiopathische pulmonale Fibrose", Der Radiologe, 2020;60:549-562.
- 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.
- 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", Scientific Reports, 2020;10(1):6276.
- G. Rubin, C. Ryerson, L. Haramati, N. Sverzellati, J. Kanne, S. Raoof, N. Schluger, A. Volpi, J. Yim, I. Martin, D. Anderson, C. Kong, T. Altes, A. Bush, S. Desai, J. Goldin, J. Goo, M. Humbert, Y. Inoue, H. Kauczor, F. Luo, P. Mazzone, M. Prokop, M. Remy-Jardin, L. Richeldi, C. Schaefer-Prokop, N. Tomiyama, A. Wells and A. Leung, "The Role of Chest Imaging in Patient Management During the COVID-19 Pandemic", Chest, 2020;158:106-116.
- 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", Scientific Reports, 2020;10:5492.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- E. Thee, D. Luttikhuizen, H. Lemij, F. Verbraak, C. Sánchez and C. Klaver, "Artificial intelligence for eye care", Nederlands Tijdschrift voor Geneeskunde, 2020.
- J. Goudsmit and J. Teuwen, "Tussen data en theorie", Tijdschrift voor Toezicht, 2020;11(1):48-53.
- 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.
- 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.
- 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.
- A. Schreuder and C. Schaefer-Prokop, "Perifissural nodules: ready for application into lung cancer CT screening?", Annals of Translational Medicine, 2020.
Preprints
- G. Bortsova, C. González-Gonzalo, S. Wetstein, F. Dubost, I. Katramados, L. Hogeweg, B. Liefers, B. van Ginneken, J. Pluim, M. Veta, C. Sánchez and M. de Bruijne, "Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors", arXiv:2006.06356, 2020.
- C. Mercan, M. Balkenhol, R. Salgado, M. Sherman, P. Vielh, W. Vreuls, A. Polonia, H. Horlings, W. Weichert, J. Carter, P. Bult, M. Christgen, C. Denkert, K. van de Vijver, J. van der Laak and F. Ciompi, "Automated Scoring of Nuclear Pleomorphism Spectrum with Pathologist-level Performance in Breast Cancer", arXiv:2012.04974, 2020.
- 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.
- A. Sekuboyina, M. Husseini, A. Bayat, M. Löffler, H. Liebl, H. Li, G. Tetteh, J. Kukačka, C. Payer, D. Stern, M. Urschler, M. Chen, D. Cheng, N. Lessmann, Y. Hu, T. Wang, D. Yang, D. Xu, F. Ambellan, T. Amiranashvili, M. Ehlke, H. Lamecker, S. Lehnert, M. Lirio, N. de Olaguer, H. Ramm, M. Sahu, A. Tack, S. Zachow, T. Jiang, X. Ma, C. Angerman, X. Wang, K. Brown, A. Kirszenberg, É. Puybareau, D. Chen, Y. Bai, B. Rapazzo, T. Yeah, A. Zhang, S. Xu, F. Hou, Z. He, C. Zeng, Z. Xiangshang, X. Liming, T. Netherton, R. Mumme, L. Court, Z. Huang, C. He, L. Wang, S. Ling, L. Huynh, N. Boutry, R. Jakubicek, J. Chmelik, S. Mulay, M. Sivaprakasam, J. Paetzold, S. Shit, I. Ezhov, B. Wiestler, B. Glocker, A. Valentinitsch, M. Rempfler, B. Menze and J. Kirschke, "VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images", arXiv:2001.09193, 2020.
- 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.
- Y. Beauferris, J. Teuwen, D. Karkalousos, N. Moriakov, M. Caan, G. Yiasemis, L. Rodrigues, A. Lopes, H. Pedrini, L. Rittner, M. Dannecker, V. Studenyak, F. Gröger, D. Vyas, S. Faghih-Roohi, A. Jethi, J. Raju, M. Sivaprakasam, M. Lasby, N. Nogovitsyn, W. Loos, R. Frayne and R. Souza, "Multi-Coil MRI Reconstruction Challenge -- Assessing Brain MRI Reconstruction Models and their Generalizability to Varying Coil Configurations", arXiv:2011.07952, 2020.
- N. Lessmann and B. van Ginneken, "Random smooth gray value transformations for cross modality learning with gray value invariant networks", arXiv:2003.06158, 2020.
- M. Muckley, B. Riemenschneider, A. Radmanesh, S. Kim, G. Jeong, J. Ko, Y. Jun, H. Shin, D. Hwang, M. Mostapha, S. Arberet, D. Nickel, Z. Ramzi, P. Ciuciu, J. Starck, J. Teuwen, D. Karkalousos, C. Zhang, A. Sriram, Z. Huang, N. Yakubova, Y. Lui and F. Knoll, "Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction", arXiv:2012.06318, 2020.
Papers in conference proceedings
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- C. Balta, A. Rodriguez-Ruiz, C. Mieskes, N. Karssemeijer and S. Heywang-Köbrunner, "Going from double to single reading for screening exams labeled as likely normal by AI: what is the impact?", 15th International Workshop on Breast Imaging (IWBI2020), 2020.
- N. Moriakov, J. Adler and J. Teuwen, "Kernel of CycleGAN as a principal homogeneous space", International Conference on Learning Representations, 2020.
- 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.
- E. García, Y. Diez, A. Oliver, N. Karssemeijer, J. Martí, R. Martí and O. Diaz, "Evaluation of elastic parameters for breast compression using a MRI-mammography registration approach", 15th International Workshop on Breast Imaging (IWBI2020), 2020.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
Abstracts
- 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.
- 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.
- 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.
- 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.
- C. Mercan, M. Balkenhol, J. Laak and F. Ciompi, "Grading nuclear pleomorphism in breast cancer using deep learning", European Congress of Pathology, 2020.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
PhD theses
- M. Meijs, "Automated Image Analysis and Machine Learning to Detect Cerebral Vascular Pathology in 4D-CTA", PhD thesis, 2020.
- M. Balkenhol, "Tissue-based biomarker assessment for predicting prognosis of triple negative breast cancer: the additional value of artificial intelligence", PhD thesis, 2020.
- S. van Riel, "Malignancy risk estimation of screen-detected pulmonary nodules", PhD thesis, 2020.
Master theses
- L. van Eekelen, "Deep learning-based analysis of bone marrow histopathology images", Master thesis, 2020.
- A. Saha, M. Hosseinzadeh and H. Huisman, "Computer-Aided Detection of Clinically Significant Prostate Cancer in mpMRI", Master thesis, 2020.
- T. van den Hout, "Automatic muscle and fat segmentation in 3D abdominal CT images for body composition assessment", Master thesis, 2020.
- T. de Boer, "A feasibility study for Deep Learning Image Guided Guidewire Tracking for Image-guided Interventions", Master thesis, 2020.
- T. Payer, "AI-assisted PD-L1 scoring in non-small-cell lung cancer", Master thesis, 2020.
- R. Kluge, "Combining AI with Radiologists: exploring the possibilities in implementation of Computer-Aided Detection", Master thesis, 2020.
- I. Slootweg, "Patient variables related to false predictions of deep-learning assisted prostate cancer detection in MRI", Master thesis, 2020.
- K. Faryna, "Brain MRI synthesis via pathology factorization and adversarial cycle-consistent learning for data augmentation", Master thesis, 2020.
- J. Spronck, "Multi conditional lung nodule synthesis for improved nodule malignancy classification in Computed Tomography scans", Master thesis, 2020.
- M. Schilpzand, "Automatic Placenta Localisation from Ultrasound Imaging in a Resource-Limited Setting", Master thesis, 2020.
Other publications
- J. Petersen, R. Estepar, A. Schmidt-Richberg, S. Gerard, B. Lassen-Schmidt, C. Jacobs, R. Beichel and K. Mori, "Thoracic Image Analysis", 2020;12502.