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/PDF 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/PDF 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/PDF 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/PDF 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/PDF 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/PDF 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/PDF 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/PDF 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/PDF 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/PDF DOI PMID
  11. 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/PDF DOI PMID Cited by ~25
  12. 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/PDF DOI PMID Cited by ~9
  13. N. deSouza, E. Achten, A. Alberich-Bayarri, F. Bamberg, R. Boellaard, O. Clement, L. Fournier, F. Gallagher, X. Golay, C. Heussel, E. Jackson, R. Manniesing, M. Mayerhofer, E. Neri, J. O'Connor, K. Oguz, A. Persson, M. Smits, E. van Beek, C. Zech and E. of Radiology, "Validated imaging biomarkers as decision-making tools in clinical trials and routine practice: current status and recommendations from the EIBALL* subcommittee of the European Society of Radiology (ESR)", Insights into Imaging, 2019;10(1):87. Abstract/PDF DOI PMID Cited by ~3
  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/PDF DOI PMID Cited by ~28
  15. D. Tellez, G. Litjens, J. van der Laak and F. Ciompi, "Neural Image Compression for Gigapixel Histopathology Image Analysis", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019;58:101544. Abstract/PDF DOI PMID Cited by ~7
  16. 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/PDF DOI PMID
  17. 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/PDF DOI PMID Cited by ~2
  18. 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). Abstract/PDF DOI PMID Cited by ~8
  19. 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/PDF DOI PMID
  20. 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/PDF DOI PMID Cited by ~34
  21. 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/PDF DOI PMID Cited by ~2
  22. 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/PDF DOI PMID Cited by ~24
  23. H. Huisman, "Solid Science of AI Supporting Bladder Cancer CT Reading", Academic Radiology, 2019;26(9):1146-1147. Abstract/PDF DOI PMID
  24. 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/PDF DOI PMID Cited by ~6
  25. 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/PDF DOI PMID Cited by ~7
  26. 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/PDF DOI PMID Cited by ~19
  27. 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
  28. 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/PDF DOI PMID Cited by ~6
  29. 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/PDF DOI PMID Cited by ~3
  30. 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/PDF DOI PMID Cited by ~2
  31. A. Rodriguez-Ruiz, K. Lang, A. Gubern-Merida, J. Teuwen, M. Broeders, G. Gennaro, P. Clauser, T. Helbich, M. Chevalier, T. Mertelmeier, M. Wallis, I. Andersson, S. Zackrisson, I. Sechopoulos and R. Mann, "Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study", European Radiology, 2019;29(9):4825-4832. Abstract/PDF DOI PMID
  32. 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/PDF DOI PMID Cited by ~3
  33. 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/PDF DOI PMID Cited by ~8
  34. 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/PDF DOI PMID Cited by ~1
  35. 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/PDF DOI PMID Cited by ~2
  36. 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/PDF DOI PMID Cited by ~56
  37. 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/PDF DOI PMID Cited by ~19
  38. 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/PDF DOI PMID Cited by ~8
  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/PDF 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/PDF DOI PMID 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/PDF 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/PDF DOI PMID 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/PDF 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/PDF 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/PDF 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/PDF 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/PDF 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/PDF DOI PMID Cited by ~8
  49. 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/PDF DOI PMID Cited by ~9
  50. 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/PDF DOI PMID
  51. 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/PDF DOI PMID Cited by ~15
  52. 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/PDF DOI Cited by ~3
  53. S. van de Leemput, J. Teuwen, B. van Ginneken and R. Manniesing, "MemCNN: A Python/PyTorch package for creating memory-efficient invertible neural networks", JOSS, 2019;4(39):1576. Abstract/PDF DOI Cited by ~2 Code
  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/PDF DOI Cited by ~1

Preprints

  1. H. Pinckaers, B. van Ginneken and G. Litjens, "Streaming convolutional neural networks for end-to-end learning with multi-megapixel images", arXiv:1911.04432, 2019. Abstract arXiv Cited by ~3
  2. 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
  3. 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
  4. 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
  5. 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
  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. 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/PDF arXiv
  8. 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/PDF 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/PDF arXiv
  10. 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
  11. 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

Papers in conference proceedings

  1. 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/PDF DOI
  2. 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/PDF Url
  3. 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/PDF Url
  4. 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/PDF arXiv Cited by ~1
  5. 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/PDF Url Cited by ~2
  6. D. Ruhe, V. Codreanu, C. van Leeuwen, D. Podareanu, V. Saletore and J. Teuwen, "Generating CT-scans with 3D Generative Adversarial Networks Using a Supercomputer", Medical Imaging meets NeurIPS, 2019. Abstract
  7. 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/PDF Url Cited by ~1
  8. A. Hering and S. Heldmann, "Unsupervised Learning for Large Motion Thoracic CT Follow-Up Registration", Medical Imaging, 2019;10949:109491B. Abstract/PDF DOI
  9. 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/PDF Url Cited by ~2
  10. 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/PDF Url Cited by ~9
  11. 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/PDF Url Cited by ~2
  12. 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/PDF DOI
  13. E. Calli, 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/PDF DOI Cited by ~4
  14. E. Calli, 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/PDF Url Cited by ~4
  15. H. Meine and A. Hering, "Efficient prealignment of CT scans for registration through a bodypart regressor", Medical Imaging with Deep Learning, 2019. Abstract/PDF Url
  16. H. Pinckaers, W. Bulten and G. Litjens, "High resolution whole prostate biopsy classification using streaming stochastic gradient descent", Medical Imaging, 2019(1). Abstract/PDF DOI Cited by ~1
  17. 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/PDF DOI Cited by ~5
  18. M. Caballo, J. Teuwen, R. Mann and I. Sechopolous, "Breast parenchyma analysis and classification for breast masses detection using texture feature descriptors and neural networks in dedicated breast CT images", Medical Imaging, 2019. Abstract/PDF DOI
  19. 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/PDF Url

Abstracts

  1. 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/PDF
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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

PhD theses

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

Master theses

  1. K. Dercksen, "Prostate Cancer Classification and Label Scarcity", 2019. Abstract/PDF
  2. J. Winkens, "Out-of-distribution detection for computational pathology with multi-head ensembles", 2019. Abstract/PDF
  3. M. Kok, "Metastases Detection in Lymph Nodes using Transfer Learning", 2019. Abstract/PDF
  4. D. Geijs, "Tumor segmentation in fluorescent TNBC immunohistochemical multiplex images using deep learning", 2019. Abstract/PDF