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.
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.
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.
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.
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.
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,
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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.
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,
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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,
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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.
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,
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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",
Scientific Reports,
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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.
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.
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.
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.
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.
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.
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",
Scientific Reports,
2019;9(1):17858.
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.
D. Tellez, G. Litjens, P. Bándi, 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.