Publications

2019

Papers in international journals

  1. P. Bándi, M. Balkenhol, B. van Ginneken, J. van der Laak and G. Litjens, "Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks", PeerJ, 2019;7:e8242.
    Abstract DOI PMID Cited by ~2
  2. 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
  3. A. Patel, F. Schreuder, C. Klijn, M. Prokop, B. van Ginneken, H. Marquering, Y. Roos, M. Baharoglu, F. Meijer and R. Manniesing, "Intracerebral haemorrhage segmentation in non-contrast CT", Nature Scientific Reports, 2019;9(1):17858.
    Abstract DOI PMID Cited by ~1
  4. J. Bleker, T. Kwee, R. Dierckx, I. de Jong, H. Huisman and D. Yakar, "Multiparametric MRI and auto-fixed volume of interest-based radiomics signature for clinically significant peripheral zone prostate cancer", European Radiology, 2019.
    Abstract DOI PMID Cited by ~2
  5. M. Bakker, S. de Lange, R. Pijnappel, R. Mann, P. Peeters, E. Monninkhof, M. Emaus, C. Loo, R. Bisschops, M. Lobbes, M. de Jong, K. Duvivier, J. Veltman, N. Karssemeijer, H. de Koning, P. van Diest, W. Mali, M. van den Bosch, W. Veldhuis, C. van Gils and D. Group, "Supplemental MRI Screening for Women with Extremely Dense Breast Tissue", New England Journal of Medicine, 2019;381(22):2091-2102.
    Abstract DOI PMID Cited by ~43
  6. O. Debats, G. Litjens and H. Huisman, "Lymph node detection in MR Lymphography: false positive reduction using multi-view convolutional neural networks", PeerJ, 2019;7:e8052.
    Abstract DOI PMID
  7. M. Mullooly, B. Ehteshami Bejnordi, R. Pfeiffer, S. Fan, M. Palakal, M. Hada, P. Vacek, D. Weaver, J. Shepherd, B. Fan, A. Mahmoudzadeh, J. Wang, S. Malkov, J. Johnson, S. Herschorn, B. Sprague, S. Hewitt, L. Brinton, N. Karssemeijer, J. van der Laak, A. Beck, M. Sherman and G. Gierach, "Application of convolutional neural networks to breast biopsies to delineate tissue correlates of mammographic breast density", NPJ Breast Cancer, 2019;5:43.
    Abstract DOI PMID Cited by ~1
  8. 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
  9. C. Balta, R. Bouwman, M. Broeders, N. Karssemeijer, W. Veldkamp, I. Sechopoulos and R. van Engen, "Optimization of the difference-of-Gaussian channel sets for the channelized Hotelling observer", Journal of Medical Imaging, 2019;6(3):035501.
    Abstract DOI PMID Cited by ~1
  10. A. Hering, S. Kuckertz, S. Heldmann and M. Heinrich, "Memory-efficient 2.5D convolutional transformer networks for multi-modal deformable registration with weak label supervision applied to whole-heart CT and MRI scans", Computer Assisted Radiology and Surgery, 2019.
    Abstract DOI PMID
  11. D. Valkenburg, E. Runhart, N. Bax, B. Liefers, S. Lambertus, C. Sánchez, F. Cremers and C. Hoyng, "Highly variable disease courses in siblings with Stargardt disease", Ophthalmology, 2019;126(12):1712-1721.
    Abstract DOI PMID
  12. M. Hermsen, T. de Bel, M. den Boer, E. Steenbergen, J. Kers, S. Florquin, J. Roelofs, M. Stegall, M. Alexander, B. Smith, B. Smeets, L. Hilbrands and J. van der Laak, "Deep-learning based histopathologic assessment of kidney tissue", Journal of the American Society of Nephrology, 2019;30(10):1968-1979.
    Abstract DOI PMID Cited by ~25
  13. 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
  14. 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
  15. R. Philipsen, C. Sánchez, J. Melendez, W. Lew and B. van Ginneken, "Automated chest X-ray reading for tuberculosis in the Philippines to improve case detection: a cohort study", International Journal of Tuberculosis and Lung Disease, 2019;23(7):805-810.
    Abstract DOI PMID
  16. A. Halilovic, D. Verweij, A. Simons, M. Stevens-Kroef, S. Vermeulen, J. Elsink, B. Tops, I. Otte-Holler, J. van der Laak, C. van de Water, O. Boelens, M. Schlooz-Vries, J. Dijkstra, I. Nagtegaal, J. Tol, P. van Cleef, P. Span and P. Bult, "HER2, chromosome 17 polysomy and DNA ploidy status in breast cancer; a translational study", Scientific Reports, 2019;9(1):11679.
    Abstract DOI PMID Cited by ~2
  17. G. Aresta, C. Jacobs, T. Araujo, A. Cunha, I. Ramos, B. van Ginneken and A. Campilho, "iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network", Nature Scientific Reports, 2019;9(1):11591.
    Abstract DOI PMID Cited by ~8
  18. M. Meijs, S. Pegge, K. Murayama, H. Boogaarts, M. Prokop, P. Willems, R. Manniesing and F. Meijer, "Color mapping of 4D-CTA for the detection of cranial arteriovenous shunts", American Journal of Neuroradiology, 2019;40(9):1498-1504.
    Abstract DOI PMID
  19. 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
  20. V. Schreur, A. de Breuk, F. Venhuizen, C. Sánchez, C. Tack, B. Klevering, E. de Jong and C. Hoyng, "Retinal hyperreflective foci in type 1 diabetes mellitus", Retina, 2019.
    Abstract DOI PMID Cited by ~2
  21. E. Abels, L. Pantanowitz, F. Aeffner, M. Zarella, J. van der Laak, M. Bui, V. Vemuri, A. Parwani, J. Gibbs, E. Agosto-Arroyo, A. Beck and C. Kozlowski, "Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association", Journal of Pathology, 2019;249(3):286-294.
    Abstract DOI PMID Cited by ~24
  22. H. Huisman, "Solid Science of AI Supporting Bladder Cancer CT Reading", Academic Radiology, 2019;26(9):1146-1147.
    Abstract DOI PMID
  23. C. Jacobs and B. van Ginneken, "Google's lung cancer AI: a promising tool that needs further validation", Nature Reviews Clinical Oncology, 2019;16(9):532-533.
    Abstract DOI PMID Cited by ~6
  24. 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
  25. S. Saadatmand, H. Geuzinge, E. Rutgers, R. Mann, D. de van Roy Zuidewijn, H. Zonderland, R. Tollenaar, M. Lobbes, M. Ausems, M. van 't Riet, M. Hooning, I. Mares-Engelberts, E. Luiten, E. Heijnsdijk, C. Verhoef, N. Karssemeijer, J. Oosterwijk, I. Obdeijn, H. de Koning, M. Tilanus-Linthorst and F. study group, "MRI versus mammography for breast cancer screening in women with familial risk (FaMRIsc): a multicentre, randomised, controlled trial", Lancet Oncology, 2019;20(8):1136-1147.
    Abstract DOI PMID Cited by ~19
  26. N. Khalili, N. Lessmann, E. Turk, N. Claessens, R. de Heus, T. Kolk, M. Viergever, M. Benders and I. Išgum, "Automatic brain tissue segmentation in fetal MRI using convolutional neural networks", Magnetic Resonance Imaging, 2019;64:77-89.
    Abstract DOI PMID Cited by ~13
  27. I. Munsterman, M. Van Erp, G. Weijers, C. Bronkhorst, C. de Korte, J. Drenth, J. van der Laak and E. Tjwa, "A Novel Automatic Digital Algorithm that Accurately Quantifies Steatosis in NAFLD on Histopathological Whole-Slide Images", Cytometry Part B-Clinical Cytometry, 2019.
    Abstract DOI PMID Cited by ~6
  28. G. Chlebus, H. Meine, S. Thoduka, N. Abolmaali, B. van Ginneken, H. Hahn and A. Schenk, "Reducing inter-observer variability and interaction time of MR liver volumetry by combining automatic CNN-based liver segmentation and manual corrections", PLoS One, 2019;14(5):e0217228.
    Abstract DOI PMID Cited by ~3
  29. W. Sanderink, B. Laarhuis, L. Strobbe, I. Sechopoulos, P. Bult, N. Karssemeijer and R. Mann, "A systematic review on the use of the breast lesion excision system in breast disease", Insights into Imaging, 2019;10(1):49.
    Abstract DOI PMID Cited by ~2
  30. T. Heesterbeek, E. de Jong, I. Acar, J. Groenewoud, B. Liefers, C. Sánchez, T. Peto, C. Hoyng, D. Pauleikhoff, H. Hense and A. den Hollander, "Genetic risk score has added value over initial clinical grading stage in predicting disease progression in age-related macular degeneration", Nature Scientific Reports, 2019;9(1):6611.
    Abstract DOI PMID Cited by ~7
  31. L. Aprupe, G. Litjens, T. Brinker, J. van der Laak and N. Grabe, "Robust and accurate quantification of biomarkers of immune cells in lung cancer micro-environment using deep convolutional neural networks", PeerJ, 2019;7:e6335.
    Abstract DOI PMID Cited by ~3
  32. 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
  33. B. Sturm, D. Creytens, M. Cook, J. Smits, M. van Dijk, E. Eijken, E. Kurpershoek, H. Kusters-Vandevelde, A. Ooms, C. Wauters, W. Blokx and J. van der Laak, "Validation of Whole-slide Digitally Imaged Melanocytic Lesions: Does Z-Stack Scanning Improve Diagnostic Accuracy?", Journal of Pathology Informatics, 2019;10:6.
    Abstract DOI PMID Cited by ~1
  34. M. Maas, G. Litjens, A. Wright, U. Attenberger, M. Haider, T. Helbich, B. Kiefer, K. Macura, D. Margolis, A. Padhani, K. Selnaes, G. Villeirs, J. Futterer and T. Scheenen, "A Single-Arm, Multicenter Validation Study of Prostate Cancer Localization and Aggressiveness With a Quantitative Multiparametric Magnetic Resonance Imaging Approach", Investigative Radiology, 2019.
    Abstract DOI PMID Cited by ~2
  35. 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
  36. H. Bogunovic, F. Venhuizen, S. Klimscha, S. Apostolopoulos, A. Bab-Hadiashar, U. Bagci, M. Beg, L. Bekalo, Q. Chen, C. Ciller, K. Gopinath, A. Gostar, K. Jeon, Z. Ji, S. Kang, D. Koozekanani, D. Lu, D. Morley, K. Parhi, H. Park, A. Rashno, M. Sarunic, S. Shaikh, J. Sivaswamy, R. Tennakoon, S. Yadav, S. De Zanet, S. Waldstein, B. Gerendas, C. Klaver, C. Sánchez and U. Schmidt-Erfurth, "RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge", IEEE Transactions on Medical Imaging, 2019;38(8):1858-1874.
    Abstract DOI PMID Cited by ~19
  37. 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
  38. J. Gómez-Valverde, A. Antón, G. Fatti, B. Liefers, A. Herranz, A. Santos, C. Sánchez and M. Ledesma-Carbayo, "Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning", Biomedical Optics Express, 2019;10(2):892-913.
    Abstract DOI PMID Cited by ~29
  39. A. Schreuder, C. Jacobs, L. Gallardo-Estrella, M. Prokop, C. Schaefer-Prokop and B. van Ginneken, "Predicting all-cause and lung cancer mortality using emphysema score progression rate between baseline and follow-up chest CT images: A comparison of risk model performances", PLoS One, 2019;14(2):e0212756.
    Abstract DOI PMID Cited by ~1
  40. N. Lessmann, B. van Ginneken, P. de Jong and I. Išgum, "Iterative fully convolutional neural networks for automatic vertebra segmentation and identification", Medical Image Analysis, 2019;53:142-155.
    Abstract DOI PMID arXiv Cited by ~29
  41. G. Napolitano, E. Lynge, M. Lillholm, I. Vejborg, C. van Gils, M. Nielsen and N. Karssemeijer, "Change in mammographic density across birth cohorts of Dutch breast cancer screening participants", International Journal of Cancer, 2019;145(11):2954-2962.
    Abstract DOI PMID Cited by ~2
  42. W. Bulten, P. Bándi, J. Hoven, R. van de Loo, J. Lotz, N. Weiss, J. van der Laak, B. van Ginneken, C. Hulsbergen-van de Kaa and G. Litjens, "Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard", Nature Scientific Reports, 2019;9(1).
    Abstract DOI PMID arXiv Cited by ~39
  43. J. Charbonnier, E. Pompe, C. Moore, S. Humphries, B. van Ginneken, B. Make, E. Regan, J. Crapo, E. van Rikxoort and D. Lynch, "Airway wall thickening on CT: Relation to smoking status and severity of COPD", Respiratory Medicine, 2019;146:36-41.
    Abstract DOI PMID Cited by ~10
  44. N. Lessmann, P. de Jong, C. Celeng, R. Takx, M. Viergever, B. van Ginneken and I. Išgum, "Sex Differences in Coronary Artery and Thoracic Aorta Calcification and Their Association With Cardiovascular Mortality in Heavy Smokers", JACC Cardiovascular Imaging, 2019;12:1808-1817.
    Abstract DOI PMID Cited by ~6
  45. M. Dalmis, A. Gubern-Mérida, S. Vreemann, P. Bult, N. Karssemeijer, R. Mann and J. Teuwen, "Artificial Intelligence Based Classification of Breast Lesions Imaged With a Multi-Parametric Breast MRI Protocol With ultrafast DCE-MRI, T2 and DWI", Investigative Radiology, 2019;56(6):325-332.
    Abstract DOI PMID Cited by ~12
  46. T. van den Heuvel, H. Petros, S. Santini, C. de Korte and B. van Ginneken, "Automated Fetal Head Detection and Circumference Estimation from Free-Hand Ultrasound Sweeps Using Deep Learning in Resource-Limited Countries", Ultrasound in Medicine and Biology, 2019;45(3):773-785.
    Abstract DOI PMID Cited by ~15
  47. B. van Ginneken, "Deep Learning for Triage of Chest Radiographs: Should Every Institution Train Its Own System?", Radiology, 2019;290:545-546.
    Abstract DOI PMID Cited by ~4
  48. M. Tammemagi, A. Ritchie, S. Atkar-Khattra, B. Dougherty, C. Sanghera, J. Mayo, R. Yuan, D. Manos, A. McWilliams, H. Schmidt, M. Gingras, S. Pasian, L. Stewart, S. Tsai, J. M.Seely, P. Burrowes, R. Bhatia, E. A.Haider, C. Boylan, C. Jacobs, B. van Ginneken, M. Tsao, S. Lam and the Pan-Canadian Early Detection of Lung Cancer Study Group, "Predicting Malignancy Risk of Screen Detected Lung Nodules - Mean Diameter or Volume", Journal of Thoracic Oncology, 2019;14(2):203-211.
    Abstract DOI PMID Cited by ~8
  49. R. Finger, S. Schmitz-Valckenberg, M. Schmid, G. Rubin, H. Dunbar, A. Tufail, D. Crabb, A. Binns, C. Sánchez, P. Margaron, G. Normand, M. Durbin, U. Luhmann, P. Zamiri, J. Cunha-Vaz, F. Asmus, F. Holz and O. behalf of the consortium, "MACUSTAR: Development and Clinical Validation of Functional, Structural, and Patient-Reported Endpoints in Intermediate Age-Related Macular Degeneration", Ophthalmologica, 2019;241(2):61-72.
    Abstract DOI PMID Cited by ~18
  50. S. van Riel, C. Jacobs, E. Scholten, R. Wittenberg, M. Winkler Wille, B. de Hoop, R. Sprengers, O. Mets, B. Geurts, M. Prokop, C. Schaefer-Prokop and B. van Ginneken, "Observer variability for Lung-RADS categorisation of lung cancer screening CTs: impact on patient management", European Radiology, 2019;29(2):924-931.
    Abstract DOI PMID Cited by ~9
  51. V. Schreur, A. Domanian, B. Liefers, F. Venhuizen, B. Klevering, C. Hoyng, E. de Jong and T. Theelen, "Morphological and topographical appearance of microaneurysms on optical coherence tomography angiography", British Journal of Ophthalmology, 2019;103(5):630-635.
    Abstract DOI PMID
  52. R. Becks, R. Manniesing, J. Vister, S. Pegge, S. Steens, E. van Dijk, M. Prokop and F. Meijer, "Brain CT Perfusion Improves Intracranial Vessel Occlusion Detection on CT Angiography", Journal of Neuroradiology, 2019;46(2):124-129.
    Abstract DOI PMID Cited by ~15
  53. A. Patel, S. van de Leemput, M. Prokop, B. van Ginneken and R. Manniesing, "Image Level Training and Prediction: Intracranial Hemorrhage Identification in 3D Non-Contrast CT", IEEE Access, 2019;7(1):92355-92364.
    Abstract DOI Cited by ~3
  54. S. van de Leemput, M. Meijs, A. Patel, F. Meijer, B. van Ginneken and R. Manniesing, "Multiclass Brain Tissue Segmentation in 4D CT using Convolutional Neural Networks", IEEE Access, 2019;7(1):51557-51569.
    Abstract DOI Cited by ~1
  55. M. Emaus, I. Išgum, S. van Velzen, H. van den Bongard, S. Gernaat, N. Lessmann, M. Sattler, A. Teske, J. Penninkhof, H. Meijer, J. Pignol and H. Verkooijen, "Bragatston study protocol: a multicentre cohort study on automated quantification of cardiovascular calcifications on radiotherapy planning CT scans for cardiovascular risk prediction in patients with breast cancer", BMJ Open, 2019;9:e028752.
    Abstract DOI
  56. B. de Vos, J. Wolterink, T. Leiner, P. de Jong, N. Lessmann and I. Išgum, "Direct automatic coronary calcium scoring in cardiac and chest CT", IEEE Transactions on Medical Imaging, 2019;38:2127-38.
    Abstract DOI
  57. S. van de Leemput, J. Teuwen, B. van Ginneken and R. Manniesing, "MemCNN: A Python/PyTorch package for creating memory-efficient invertible neural networks", Journal of Open Source Software, 2019;4(39):1576.
    Abstract DOI Cited by ~2 Code

Preprints

  1. 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", arXiv:1910.07373, 2019.
    Abstract arXiv
  2. 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", arXiv:1903.03349, 2019.
    Abstract arXiv
  3. S. Hu, D. Worrall, S. Knegt, B. Veeling, H. Huisman and M. Welling, "Supervised uncertainty quantification for segmentation with multiple annotations", arXiv:1907.01949, 2019.
    Abstract arXiv Cited by ~12
  4. M. Argus, C. Schaefer-Prokop, D. Lynch and B. van Ginneken, "Function Follows Form: Regression from Complete Thoracic Computed Tomography Scans", arXiv:1909.12047, 2019.
    Abstract arXiv
  5. A. Simpson, M. Antonelli, S. Bakas, M. Bilello, K. Farahani, B. van Ginneken, A. Kopp-Schneider, B. Landman, G. Litjens, B. Menze, O. Ronneberger, R. Summers, P. Bilic, P. Christ, R. Do, M. Gollub, J. Golia-Pernicka, S. Heckers, W. Jarnagin, M. McHugo, S. Napel, E. Vorontsov, L. Maier-Hein and M. Cardoso, "A large annotated medical image dataset for the development and evaluation of segmentation algorithms", arXiv:1902.09063, 2019.
    Abstract arXiv Cited by ~75
  6. P. Bilic, P. Christ, E. Vorontsov, G. Chlebus, H. Chen, Q. Dou, C. Fu, X. Han, P. Heng, J. Hesser, S. Kadoury, T. Konopczynski, M. Le, C. Li, X. Li, J. Lipkova, J. Lowengrub, H. Meine, J. Moltz, C. Pal, M. Piraud, X. Qi, J. Qi, M. Rempfler, K. Roth, A. Schenk, A. Sekuboyina, E. Vorontsov, P. Zhou, C. Hulsemeyer, M. Beetz, F. Ettlinger, F. Gruen, G. Kaissis, F. Lohofer, R. Braren, J. Holch, F. Hofmann, W. Sommer, V. Heinemann, C. Jacobs, G. Humpire Mamani, B. van Ginneken, G. Chartrand, A. Tang, M. Drozdzal, A. Ben-Cohen, E. Klang, M. Amitai, E. Konen, H. Greenspan, J. Moreau, A. Hostettler, L. Soler, R. Vivanti, A. Szeskin, N. Lev-Cohain, J. Sosna, L. Joskowicz and B. Menze, "The Liver Tumor Segmentation Benchmark (LiTS)", arXiv:1901.04056, 2019.
    Abstract arXiv Cited by ~82
  7. B. Liefers, J. Colijn, C. González-Gonzalo, T. Verzijden, 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", arXiv:1908.05621, 2019.
    Abstract arXiv
  8. P. Putzky, D. Karkalousos, J. Teuwen, N. Moriakov, B. Bakker, M. Caan and M. Welling, "i-RIM applied to the fastMRI challenge", arXiv:1910.08952, 2019.
    Abstract arXiv
  9. 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", arXiv:1910.04071, 2019.
    Abstract arXiv
  10. R. Dilz, L. Schröder, N. Moriakov, J. Sonke and J. Teuwen, "Learned SIRT for Cone Beam Computed Tomography Reconstruction", arXiv:1908.10715, 2019.
    Abstract arXiv

Papers in conference proceedings

  1. B. Liefers, C. González-Gonzalo, C. Klaver, B. van Ginneken and C. Sánchez, "Dense Segmentation in Selected Dimensions: Application to Retinal Optical Coherence Tomography", Medical Imaging with Deep Learning, 2019;102:337-346.
    Abstract Url Cited by ~2
  2. A. Hering, S. Kuckertz, S. Heldmann and M. Heinrich, "Enhancing Label-Driven Deep Deformable Image Registration with Local Distance Metrics for State-of-the-Art Cardiac Motion Tracking", Bildverarbeitung für die Medizin, 2019:309-314.
    Abstract arXiv
  3. 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
  4. A. Hering, B. van Ginneken and S. Heldmann, "mlVIRNET: Multilevel Variational Image Registration Network", Medical Image Computing and Computer-Assisted Intervention, 2019;11769:257-265.
    Abstract DOI arXiv Cited by ~5
  5. N. Moriakov, K. Michielsen, R. Mann, J. Adler, I. Sechopolous and J. Teuwen, "Deep learning framework for digital breast tomosynthesis reconstruction", Medical Imaging, 2019.
    Abstract DOI arXiv
  6. E. Çallı, E. Sogancioglu, E. Scholten, K. Murphy and B. van Ginneken, "Handling label noise through model confidence and uncertainty: application to chest radiograph classification", Medical Imaging, 2019(1).
    Abstract DOI Cited by ~4
  7. H. Meine and A. Hering, "Efficient prealignment of CT scans for registration through a bodypart regressor", Medical Imaging with Deep Learning, 2019.
    Abstract Url
  8. 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
  9. K. Dercksen, W. Bulten and G. Litjens, "Dealing with Label Scarcity in Computational Pathology: A Use Case in Prostate Cancer Classification", Medical Imaging with Deep Learning, 2019.
    Abstract Url Cited by ~2
  10. A. Hering and S. Heldmann, "Unsupervised Learning for Large Motion Thoracic CT Follow-Up Registration", Medical Imaging, 2019;10949:109491B.
    Abstract DOI
  11. E. Çallı, K. Murphy, E. Sogancioglu and B. van Ginneken, "FRODO: Free rejection of out-of-distribution samples: application to chest x-ray analysis", Medical Imaging with Deep Learning, 2019.
    Abstract Url Cited by ~4
  12. N. Lessmann, J. Wolterink, M. Zreik, M. Viergever, B. van Ginneken and I. Isgum, "Vertebra partitioning with thin-plate spline surfaces steered by a convolutional neural network", Medical Imaging with Deep Learning, 2019.
    Abstract arXiv Cited by ~1
  13. S. van Velzen, M. Zreik, N. Lessmann, M. Viergever, P. de Jong, H. Verkooijen and I. Išgum, "Direct prediction of cardiovascular mortality from low-dose chest CT using deep learning", Medical Imaging, 2019.
    Abstract DOI
  14. H. Pinckaers, W. Bulten and G. Litjens, "High resolution whole prostate biopsy classification using streaming stochastic gradient descent", Medical Imaging, 2019(1).
    Abstract DOI Cited by ~1
  15. M. Hosseinzadeh, P. Brand and H. Huisman, "Effect of Adding Probabilistic Zonal Prior in Deep Learning-based Prostate Cancer Detection", Medical Imaging with Deep Learning, 2019.
    Abstract Url Cited by ~1
  16. T. van der Ouderaa, D. Worrall and B. van Ginneken, "Chest CT Super-resolution and Domain-adaptation using Memory-efficient 3D Reversible GANs", Medical Imaging with Deep Learning, 2019.
    Abstract Url
  17. T. van den Heuvel, C. de Korte and B. van Ginneken, "Automated interpretation of prenatal ultrasound using a predefined acquisition protocol in resource-limited countries", Medical Imaging with Deep Learning, 2019.
    Abstract Url
  18. J. van Vugt, E. Marchiori, R. Mann, A. Gubern-Merida, N. Moriakov and J. Teuwen, "Vendor-independent soft tissue lesion detection using weakly supervised and unsupervised adversarial domain adaptation", Medical Imaging, 2019.
    Abstract DOI
  19. T. de Bel, M. Hermsen, J. Kers, J. van der Laak and G. Litjens, "Stain-Transforming Cycle-Consistent Generative Adversarial Networks for Improved Segmentation of Renal Histopathology", Medical Imaging with Deep Learning, 2019.
    Abstract Url Cited by ~9

Abstracts

  1. J. Engelberts, C. González-Gonzalo, C. Sánchez and M. van Grinsven, "Automatic Segmentation of Drusen and Exudates on Color Fundus Images using Generative Adversarial Networks", Association for Research in Vision and Ophthalmology, 2019.
    Abstract
  2. B. Liefers, J. Colijn, C. González-Gonzalo, A. Vaidyanathan, H. van Zeeland, P. Mitchell, C. Klaver and C. Sánchez, "Prediction of areas at risk of developing geographic atrophy in color fundus images using deep learning", Association for Research in Vision and Ophthalmology, 2019.
    Abstract
  3. M. Silva, G. Milanese, F. Sabia, C. Jacobs, B. van Ginneken, M. Prokop, C. Schaefer-Prokop, S. Sestini, A. Marchiano, N. Sverzellati and U. Pastorino, "Lung Cancer Screening in NLST Eligibles: Tailoring Annual Low-Dose Computed Tomography by Post-Test Risk Stratification", Annual Meeting of the Radiological Society of North America, 2019.
    Abstract
  4. S. van Velzen, N. Lessmann, M. Emaus, H. van den Bongard, H. Verkooijen and I. Išgum, "Deep learning for calcium scoring in radiotherapy treatment planning CT scans in breast cancer patients", Annual Meeting of the Radiological Society of North America, 2019.
    Abstract
  5. M. Silva, G. Milanese, F. Sabia, C. Jacobs, B. van Ginneken, M. Prokop, C. Schaefer-Prokop, A. Marchiano, N. Sverzellati and U. Pastorino, "Lung cancer risk after baseline round of screening: Only 20% of NLST eligibles require annual round", Annual Meeting of the European Society of Thoracic Imaging, 2019.
    Abstract
  6. M. Hermsen, T. de Bel, M. den Boer, E. Steenbergen, J. Kers, S. Florquin, J. Roelofs, M. Stegall, M. Alexander, B. Smith, B. Smeets, L. Hilbrands and J. van der Laak, "Deep learning-based histopathological assessment of renal tissue", American Society of Nephrology Kidney Week 2019, 2019.
    Abstract
  7. C. van 't Klooster, H. Nathoe, J. Hjortnaes, M. Bots, I. Isgum, N. Lessmann, Y. van der Graaf, T. Leiner and F. Visseren, "Prevalence and risk factors of multifocal cardiovascular calcification in patients at high cardiovascular risk", European Society of Cardiology Congress, 2019.
    Abstract
  8. G. Chlebus, G. Humpire Mamani, A. Schenk, B. van Ginneken and H. Meine, "Mimicking radiologists to improve the robustness of deep-learning based automatic liver segmentation", Annual Meeting of the Radiological Society of North America, 2019.
    Abstract
  9. D. Valkenburg, E. Runhart, B. Liefers, S. Lambertus, C. Sánchez, F. Cremers, B. Nathalie M and C. Hoyng, "Familial discordance in disease phenotype in siblings with Stargardt disease", Association for Research in Vision and Ophthalmology, 2019.
    Abstract
  10. W. Bulten, H. Pinckaers, C. Hulsbergen-van de Kaa and G. Litjens, "Automated Gleason Grading of Prostate Biopsies Using Deep Learning", United States and Canadian Academy of Pathology (USCAP) 108th Annual Meeting, 2019.
    Abstract Cited by ~9
  11. H. van Zeeland, J. Meakin, B. Liefers, C. González-Gonzalo, A. Vaidyanathan, B. van Ginneken, C. Klaver and C. Sánchez, "EyeNED workstation: Development of a multi-modal vendor-independent application for annotation, spatial alignment and analysis of retinal images", Association for Research in Vision and Ophthalmology, 2019.
    Abstract Cited by ~1
  12. T. van den Heuvel, B. van Ginneken and C. de Korte, "Improving Maternal Care In Resource-Limited Settings Using A Low-Cost Ultrasound Device And Machine Learning", Dutch Bio-Medical Engineering Conference, 2019.
    Abstract
  13. 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", Annual Meeting of the International Society for Magnetic Resonance in Medicine, 2019.
    Abstract
  14. C. Jacobs, E. Scholten, A. Schreuder, M. Prokop and B. van Ginneken, "An observer study comparing radiologists with the prize-winning lung cancer detection algorithms from the 2017 Kaggle Data Science Bowl", Annual Meeting of the Radiological Society of North America, 2019.
    Abstract
  15. C. González-Gonzalo, B. Liefers, A. Vaidyanathan, H. van Zeeland, C. Klaver and C. Sánchez, "Opening the "black box" of deep learning in automated screening of eye diseases", Association for Research in Vision and Ophthalmology, 2019.
    Abstract Url
  16. S. van Velzen, J. Terry, B. de Vos, N. Lessmann, S. Nair, A. Correa, H. Verkooijen, J. Carr and I. Išgum, "Automatic prediction of coronary heart disease events using coronary and thoracic aorta calcium among african americans in the Jackson Heart Study", Annual Meeting of the Radiological Society of North America, 2019.
    Abstract
  17. N. Khalili, N. Lessmann, E. Turk, M. Viergever, M. Benders and I. Išgum, "Brain tissue segmentation in fetal MRI using convolutional neural networks with simulated intensity inhomogeneities", Annual Meeting of the International Society for Magnetic Resonance in Medicine, 2019.
    Abstract
  18. D. Grob, L. Oostveen, C. Jacobs, M. Prokop, C. Schaefer-Prokop, I. Sechopoulos and M. Brink, "Intra-patient comparison of pulmonary nodule enhancement in subtraction CT and dual-energy CT", Annual Meeting of the European Society of Thoracic Imaging, 2019.
    Abstract
  19. 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
  20. C. Jacobs and B. van Ginneken, "Deep learning for detection and characterization of lung nodules", Annual Meeting of the European Society of Thoracic Imaging, 2019.
    Abstract

PhD theses

  1. C. Balta, "Objective image quality assessment in X-ray breast imaging", 2019.
    Abstract
  2. T. van den Heuvel, "Automated low-cost ultrasound: improving antenatal care in resource-limited settings", 2019.
    Abstract Url
  3. N. Lessmann, "Machine Learning based quantification of extrapulmonary diseases in chest CT", 2019.
    Abstract Url
  4. E. Smit, "Feasibility of a single-acquisition CT stroke protocol", 2019.
    Abstract
  5. M. Dalmis, "Automated Analysis of Breast MRI From traditional methods into deep learning", 2019.
    Abstract Url
  6. D. Grob, "Functional CT Imaging of the Lung: Substraction CT as a novel technique", 2019.
    Abstract
  7. J. van Zelst, "Automated 3D breast ultrasound Advances in breast cancer detection, diagnosis and screening", 2019.
    Abstract
  8. L. Estrella, "Quantification of COPD biomarkers in thoracic CT scans", 2019.
    Abstract Url
  9. F. Venhuizen, "Machine Learning for Quantification of Age-Related Macular Degeneration Imaging Biomarkers in Optical Coherence Tomography", 2019.
    Abstract
  10. R. Philipsen, "Automated chest radiography reading. Improvements, validation, and cost-effectiveness analysis", 2019.
    Abstract Url
  11. A. Ruiz, "Artificial intelligence & tomosynthesis for breast cancer detection", 2019.
    Abstract Url

Master theses

  1. K. Dercksen, "Prostate Cancer Classification and Label Scarcity", 2019.
    Abstract
  2. M. van Rijthoven, "Cancer research in digital pathology using convolutional neural networks", 2019.
    Abstract
  3. J. Winkens, "Out-of-distribution detection for computational pathology with multi-head ensembles", 2019.
    Abstract
  4. M. Kok, "Metastases Detection in Lymph Nodes using Transfer Learning", 2019.
    Abstract
  5. P. Sonsma, "Lymphocyte detection in hematoxylin-eosin stained histopathological images of breast cancer", 2019.
    Abstract
  6. D. Geijs, "Tumor segmentation in fluorescent TNBC immunohistochemical multiplex images using deep learning", 2019.
    Abstract