Publications of Geert Litjens

Papers in international journals

  1. M. Balkenhol, F. Ciompi, Ż. Świderska-Chadaj, R. van de Loo, M. Intezar, I. Otte-Höller, D. Geijs, J. Lotz, N. Weiss, T. de Bel, G. Litjens, P. Bult and J. van der Laak, "Optimized tumour infiltrating lymphocyte assessment for triple negative breast cancer prognostics.", BREAST, 2021;56:78-87. Abstract DOI PMID
  2. 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, 2021;43(2):567-578. Abstract DOI PMID
  3. Z. Swiderska-Chadaj, K. Hebeda, M. van den Brand and G. Litjens, "Artificial intelligence to detect MYC translocation in slides of diffuse large B-cell lymphoma", Virchows Archiv, 2020. Abstract DOI PMID
  4. Z. Swiderska-Chadaj, T. de Bel, L. Blanchet, A. Baidoshvili, D. Vossen, J. van der Laak and G. Litjens, "Impact of rescanning and normalization on convolutional neural network performance in multi-center, whole-slide classification of prostate cancer", Scientific Reports, 2020;10(1):14398. Abstract DOI PMID
  5. H. Pinckaers, B. van Ginneken and G. Litjens, "Streaming convolutional neural networks for end-to-end learning with multi-megapixel images", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020. Abstract DOI PMID GitHub
  6. W. Bulten, M. Balkenhol, J. Belinga, A. Brilhante, A. Çakır, L. Egevad, M. Eklund, X. Farré, K. Geronatsiou, V. Molinié, G. Pereira, P. Roy, G. Saile, P. Salles, E. Schaafsma, J. Tschui, A. Vos, B. Delahunt, H. Samaratunga, D. Grignon, A. Evans, D. Berney, C. Pan, G. Kristiansen, J. Kench, J. Oxley, K. Leite, J. McKenney, P. Humphrey, S. Fine, T. Tsuzuki, M. Varma, M. Zhou, E. Comperat, D. Bostwick, K. Iczkowski, C. Magi-Galluzzi, J. Srigley, H. Takahashi, T. van der Kwast, H. van Boven, R. Vink, J. van der Laak, C. der Hulsbergen-van Kaa and G. Litjens, "Artificial Intelligence Assistance Significantly Improves Gleason Grading of Prostate Biopsies by Pathologists", Modern Pathology, 2020. Abstract DOI PMID
  7. G. van Leenders, T. van der Kwast, D. Grignon, A. Evans, G. Kristiansen, C. Kweldam, G. Litjens, J. McKenney, J. Melamed, N. Mottet, G. Paner, H. Samaratunga, I. Schoots, J. Simko, T. Tsuzuki, M. Varma, A. Warren, T. Wheeler, S. Williamson, K. Iczkowski and I. Members, "The 2019 International Society of Urological Pathology (ISUP) Consensus Conference on Grading of Prostatic Carcinoma.", American Journal of Surgical Pathology, 2020;44(8):e87-e99. Abstract DOI PMID
  8. W. Bulten, H. Pinckaers, H. van Boven, R. Vink, T. de Bel, B. van Ginneken, J. van der Laak, C. de Hulsbergen-van Kaa and G. Litjens, "Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study", Lancet Oncology, 2020;21(2):233-241. Abstract DOI PMID Cited by ~29 Algorithm
  9. 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
  10. 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
  11. 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
  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 DOI PMID Cited by ~9
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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 Cited by ~39
  19. P. Bándi, O. Geessink, Q. Manson, M. van Dijk, M. Balkenhol, M. Hermsen, B. Bejnordi, B. Lee, K. Paeng, A. Zhong, Q. Li, F. Zanjani, S. Zinger, K. Fukuta, D. Komura, V. Ovtcharov, S. Cheng, S. Zeng, J. Thagaard, A. Dahl, H. Lin, H. Chen, L. Jacobsson, M. Hedlund, M. Cetin, E. Halici, H. Jackson, R. Chen, F. Both, J. Franke, H. Kusters-Vandevelde, W. Vreuls, P. Bult, B. van Ginneken, J. van der Laak and G. Litjens, "From detection of individual metastases to classification of lymph node status at the patient level: the CAMELYON17 challenge", IEEE Transactions on Medical Imaging, 2018;38(2):550-560. Abstract DOI PMID Cited by ~54
  20. D. Tellez, M. Balkenhol, I. Otte-Holler, R. van de Loo, R. Vogels, P. Bult, C. Wauters, W. Vreuls, S. Mol, N. Karssemeijer, G. Litjens, J. van der Laak and F. Ciompi, "Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks", IEEE Transactions on Medical Imaging, 2018;37(9):2126 - 2136. Abstract DOI PMID Cited by ~45
  21. G. Litjens, P. Bandi, B. Ehteshami Bejnordi, O. Geessink, M. Balkenhol, P. Bult, A. Halilovic, M. Hermsen, R. van de Loo, R. Vogels, Q. Manson, N. Stathonikos, A. Baidoshvili, P. van Diest, C. Wauters, M. van Dijk and J. van der Laak, "1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset", GigaScience, 2018;7(6):1-8. Abstract DOI PMID Cited by ~48
  22. B. Bejnordi, G. Litjens and J. van der Laak, "Machine Learning Compared With Pathologist Assessment-Reply", Journal of the American Medical Association, 2018;319(16):1726. Abstract DOI PMID Cited by ~1
  23. B. Bejnordi, G. Zuidhof, M. Balkenhol, M. Hermsen, P. Bult, B. van Ginneken, N. Karssemeijer, G. Litjens and J. van der Laak, "Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images", Journal of Medical Imaging, 2017;4(4):044504. Abstract DOI PMID Cited by ~75
  24. B. Ehteshami Bejnordi, M. Veta, P. van Diest, B. van Ginneken, N. Karssemeijer, G. Litjens, J. van der Laak, T. Consortium, M. Hermsen, Q. Manson, M. Balkenhol, O. Geessink, N. Stathonikos, M. van Dijk, P. Bult, F. Beca, A. Beck, D. Wang, A. Khosla, R. Gargeya, H. Irshad, A. Zhong, Q. Dou, Q. Li, H. Chen, H. Lin, P. Heng, C. Haß, E. Bruni, Q. Wong, U. Halici, M. Öner, R. Cetin-Atalay, M. Berseth, V. Khvatkov, A. Vylegzhanin, O. Kraus, M. Shaban, N. Rajpoot, R. Awan, K. Sirinukunwattana, T. Qaiser, Y. Tsang, D. Tellez, J. Annuscheit, P. Hufnagl, M. Valkonen, K. Kartasalo, L. Latonen, P. Ruusuvuori, K. Liimatainen, S. Albarqouni, B. Mungal, A. George, S. Demirci, N. Navab, S. Watanabe, S. Seno, Y. Takenaka, H. Matsuda, H. Ahmady Phoulady, V. Kovalev, A. Kalinovsky, V. Liauchuk, G. Bueno, M. Fernandez-Carrobles, I. Serrano, O. Deniz, D. Racoceanu and R. Venâncio, "Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer", Journal of the American Medical Association, 2017;318(22):2199-2210. Abstract DOI PMID Cited by ~791
  25. G. Litjens, T. Kooi, B. Ehteshami Bejnordi, A. Setio, F. Ciompi, M. Ghafoorian, J. van der Laak, B. van Ginneken and C. Sánchez, "A Survey on Deep Learning in Medical Image Analysis", Medical Image Analysis, 2017;42:60-88. Abstract DOI PMID Cited by ~3681
  26. M. Ghafoorian, N. Karssemeijer, T. Heskes, I. van Uden, C. Sánchez, G. Litjens, F. de Leeuw, B. van Ginneken, E. Marchiori and B. Platel, "Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities", Nature Scientific Reports, 2017;7(1):5110. Abstract DOI PMID Cited by ~117
  27. S. Laban, G. Giebel, N. Klümper, A. Schröck, J. Doescher, G. Spagnoli, J. Thierauf, M. Theodoraki, R. Remark, S. Gnjatic, R. Krupar, A. Sikora, G. Litjens, N. Grabe, G. Kristiansen, F. Bootz, P. Schuler, C. Brunner, J. Brägelmann, T. Hoffmann and S. Perner, "MAGE expression in head and neck squamous cell carcinoma primary tumors, lymph node metastases and respective recurrences: implications for immunotherapy", Oncotarget, 2017;8:14719-14735. Abstract DOI PMID Cited by ~17
  28. S. Steens, E. Bekers, W. Weijs, G. Litjens, A. Veltien, A. Maat, G. van den Broek, J. van der Laak, J. Fütterer, C. van der Kaa, M. Merkx and R. Takes, "Evaluation of tongue squamous cell carcinoma resection margins using ex-vivo MR.", International Journal of Computer Assisted Radiology and Surgery, 2017;12(5):821-828. Abstract DOI PMID
  29. M. Dalmis, G. Litjens, K. Holland, A. Setio, R. Mann, N. Karssemeijer and A. Gubern-Mérida, "Using deep learning to segment breast and fibroglandular tissue in MRI volumes", Medical Physics, 2017;44(2):533-546. Abstract DOI PMID Cited by ~113
  30. T. Kooi, G. Litjens, B. van Ginneken, A. Gubern-Mérida, C. Sánchez, R. Mann, A. den Heeten and N. Karssemeijer, "Large scale deep learning for computer aided detection of mammographic lesions", Medical Image Analysis, 2017;35:303-312. Abstract DOI PMID Cited by ~432
  31. R. Remark, T. Merghoub, N. Grabe, G. Litjens, D. Damotte, J. Wolchok, M. Merad and S. Gnjatic, "In-depth tissue profiling using multiplexed immunohistochemical consecutive staining on single slide", Science Immunology, 2016;1(1):aaf6925-aaf6925. Abstract DOI PMID Cited by ~63
  32. O. Debats, A. Fortuin, H. Meijer, T. Hambrock, G. Litjens, J. Barentsz and H. Huisman, "Intranodal signal suppression in pelvic MR lymphography of prostate cancer patients: a quantitative comparison of ferumoxtran-10 and ferumoxytol", PeerJ, 2016;4:e2471. Abstract DOI PMID Cited by ~7
  33. O. Debats, M. Meijs, G. Litjens and H. Huisman, "Automated multistructure atlas-assisted detection of lymph nodes using pelvic MR lymphography in prostate cancer patients", Medical Physics, 2016;43(6):3132. Abstract DOI PMID Cited by ~2
  34. G. Litjens, C. Sánchez, N. Timofeeva, M. Hermsen, I. Nagtegaal, I. Kovacs, C. Hulsbergen-van de Kaa, P. Bult, B. van Ginneken and J. van der Laak, "Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis", Nature Scientific Reports, 2016;6:26286. Abstract DOI PMID Cited by ~490
  35. B. Bejnordi, M. Balkenhol, G. Litjens, R. Holland, P. Bult, N. Karssemeijer and J. van der Laak, "Automated Detection of DCIS in Whole-Slide H&E Stained Breast Histopathology Images", IEEE Transactions on Medical Imaging, 2016;35(9):2141-2150. Abstract DOI PMID Cited by ~55
  36. A. Setio, F. Ciompi, G. Litjens, P. Gerke, C. Jacobs, S. van Riel, M. Wille, M. Naqibullah, C. Sánchez and B. van Ginneken, "Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks", IEEE Transactions on Medical Imaging, 2016;35(5):1160-1169. Abstract DOI PMID Cited by ~605
  37. B. Bejnordi, G. Litjens, N. Timofeeva, I. Otte-Holler, A. Homeyer, N. Karssemeijer and J. van der Laak, "Stain specific standardization of whole-slide histopathological images", IEEE Transactions on Medical Imaging, 2016;35(2):404-415. Abstract DOI PMID Cited by ~128
  38. G. Litjens, R. Elliott, N. Shih, M. Feldman, T. Kobus, C. Hulsbergen-van de Kaa, J. Barentsz, H. Huisman and A. Madabhushi, "Computer-extracted Features Can Distinguish Noncancerous Confounding Disease from Prostatic Adenocarcinoma at Multiparametric MR Imaging.", Radiology, 2016;278(1):135-145. Abstract DOI PMID
  39. G. Litjens, J. Barentsz, N. Karssemeijer and H. Huisman, "Clinical evaluation of a computer-aided diagnosis system for determining cancer aggressiveness in prostate MRI", European Radiology, 2015;25(11):3187-3199. Abstract DOI PMID Cited by ~47
  40. E. Vos, T. Kobus, G. Litjens, T. Hambrock, C. de Hulsbergen-van Kaa, J. Barentsz, M. Maas and T. Scheenen, "Multiparametric Magnetic Resonance Imaging for Discriminating Low-Grade From High-Grade Prostate Cancer", Investigative Radiology, 2015;50:490-497. Abstract DOI PMID
  41. G. Litjens, H. Huisman, R. Elliott, N. Shih, M. Feldman, S. Viswanath, J. Fütterer, J. Bomers and A. Madabhushi, "Quantitative identification of magnetic resonance imaging features of prostate cancer response following laser ablation and radical prostatectomy", Journal of Medical Imaging, 2014;1(3):035001-035001. Abstract DOI PMID Cited by ~16
  42. G. Litjens, O. Debats, J. Barentsz, N. Karssemeijer and H. Huisman, "Computer-aided detection of prostate cancer in MRI", IEEE Transactions on Medical Imaging, 2014;33(5):1083-1092. Abstract DOI PMID Cited by ~234
  43. G. Litjens, R. Toth, W. van de Ven, C. Hoeks, S. Kerkstra, B. van Ginneken, G. Vincent, G. Guillard, N. Birbeck, J. Zhang, R. Strand, F. Malmberg, Y. Ou, C. Davatzikos, M. Kirschner, F. Jung, J. Yuan, W. Qiu, Q. Gao, P. Edwards, B. Maan, F. van der Heijden, S. Ghose, J. Mitra, J. Dowling, D. Barratt, H. Huisman and A. Madabhushi, "Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge", Medical Image Analysis, 2014;18(2):359-373. Abstract DOI PMID Cited by ~275
  44. E. Vos, G. Litjens, T. Kobus, T. Hambrock, C. Kaa, J. Barentsz, H. Huisman and T. Scheenen, "Assessment of Prostate Cancer Aggressiveness Using Dynamic Contrast-enhanced Magnetic Resonance Imaging at 3T", European Urology, 2013;64:448-455. Abstract PMID Url Cited by ~150
  45. K. Nagel, M. Schouten, T. Hambrock, G. Litjens, C. Hoeks, B. Haken, J. Barentsz and J. Fütterer, "Differentiation of Prostatitis and Prostate Cancer by Using Diffusion-weighted MR Imaging and MR-guided Biopsy at 3 T", RADIOLOGY, 2013;267:164-172. Abstract DOI PMID
  46. G. Litjens, T. Hambrock, C. de Hulsbergen-van Kaa, J. Barentsz and H. Huisman, "Interpatient Variation in Normal Peripheral Zone Apparent Diffusion Coefficient: Effect on the Prediction of Prostate Cancer Aggressiveness", Radiology, 2012;265(1):260-266. Abstract DOI PMID Cited by ~60
  47. O. Debats, G. Litjens, J. Barentsz, N. Karssemeijer and H. Huisman, "Automated 3-Dimensional Segmentation of Pelvic Lymph Nodes in Magnetic Resonance Images", Medical Physics, 2011;38(11):6178-6187. Abstract DOI PMID Cited by ~12

Preprints

  1. 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

Papers in conference proceedings

  1. L. van Eekelen, H. Pinckaers, K. Hebeda and G. Litjens, "Multi-class semantic cell segmentation and classification of aplasia in bone marrow histology images", Medical Imaging, 2020;11320:113200B. Abstract DOI
  2. Z. Swiderska-Chadaj, K. Hebeda, M. van den Brand and G. Litjens, "Predicting MYC translocation in HE specimens of diffuse large B-cell lymphoma through deep learning", Medical Imaging, 2020;11320:1132010. Abstract DOI
  3. J. Linmans, J. van der Laak and G. Litjens, "Efficient Out-of-Distribution Detection in Digital Pathology Using Multi-Head Convolutional Neural Networks", Medical Imaging with Deep Learning, 2020:465-478. Abstract Url
  4. 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
  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 Url Cited by ~2
  6. 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
  7. D. Geijs, M. Intezar, J. van der Laak and G. Litjens, "Automatic color unmixing of IHC stained whole slide images", Medical Imaging, 2018;10581. Abstract DOI Cited by ~3
  8. Z. Swiderska-Chadaj, H. Pinckaers, M. van Rijthoven, M. Balkenhol, M. Melnikova, O. Geessink, Q. Manson, G. Litjens, J. van der Laak and F. Ciompi, "Convolutional Neural Networks for Lymphocyte detection in Immunohistochemically Stained Whole-Slide Images", Medical Imaging with Deep Learning, 2018. Abstract Url Cited by ~5
  9. D. Tellez, M. Balkenhol, N. Karssemeijer, G. Litjens, J. van der Laak and F. Ciompi, "H&E stain augmentation improves generalization of convolutional networks for histopathological mitosis detection", Medical Imaging, 2018;10581. Abstract DOI
  10. W. Bulten, C. de Kaa, J. van der Laak and G. Litjens, "Automated segmentation of epithelial tissue in prostatectomy slides using deep learning", Medical Imaging, 2018;10581:105810S. Abstract DOI Cited by ~12
  11. W. Bulten and G. Litjens, "Unsupervised Prostate Cancer Detection on H&E using Convolutional Adversarial Autoencoders", Medical Imaging with Deep Learning, 2018. Abstract Url Cited by ~8
  12. P. Bándi, R. van de Loo, M. Intezar, D. Geijs, F. Ciompi, B. van Ginneken, J. van der Laak and G. Litjens, "Comparison of Different Methods for Tissue Segmentation In Histopathological Whole-Slide Images", IEEE International Symposium on Biomedical Imaging, 2017:591-595. Abstract DOI Cited by ~14
  13. F. Ciompi, O. Geessink, B. Bejnordi, G. de Souza, A. Baidoshvili, G. Litjens, B. van Ginneken, I. Nagtegaal and J. van der Laak, "The importance of stain normalization in colorectal tissue classification with convolutional networks", IEEE International Symposium on Biomedical Imaging, 2017:160-163. Abstract DOI Cited by ~75
  14. G. Litjens, K. Safferling and N. Grabe, "Automated robust registration of grossly misregistered whole-slide images with varying stains", Medical Imaging, 2016;9791:979103. Abstract DOI
  15. B. Bejnordi, G. Litjens, M. Hermsen, N. Karssemeijer and J. van der Laak, "A multi-scale superpixel classification approach for region of interest detection in whole slide histopathology images", Medical Imaging, 2015;9420:94200H. Abstract DOI
  16. G. Litjens, B. Bejnordi, N. Timofeeva, G. Swadi, I. Kovacs, C. de Hulsbergen-van Kaa and J. van der Laak, "Automated detection of prostate cancer in digitized whole-slide images of H&E-stained biopsy specimens", Medical Imaging, 2015;9420:94200B. Abstract DOI
  17. G. Litjens, H. Huisman, R. Elliott, N. Shih, M. Feldman, Fütterer, J. Bomers and A. Madabhushi, "Distinguishing benign confounding treatment changes from residual prostate cancer on MRI following laser ablation", Medical Imaging, 2014;9036:90361D. Abstract DOI Cited by ~3
  18. G. Litjens, R. Elliott, N. Shih, M. Feldman, J. Barentsz, C. - van de Hulsbergen Kaa, I. Kovacs, H. Huisman and A. Madabhushi, "Distinguishing prostate cancer from benign confounders via a cascaded classifier on multi-parametric MRI", Medical Imaging, 2014;9035:903512. Abstract DOI Cited by ~18
  19. G. Litjens, J. Barentsz, N. Karssemeijer and H. Huisman, "Automated computer-aided detection of prostate cancer in MR images: from a whole-organ to a zone-based approach", Medical Imaging, 2012;8315(1):83150G-83150G-6. Abstract DOI Cited by ~24
  20. G. Litjens, O. Debats, W. van de Ven, N. Karssemeijer and H. Huisman, "A pattern recognition approach to zonal segmentation of the prostate on MRI", Medical Image Computing and Computer-Assisted Intervention, 2012;7511:413-420. Abstract DOI Cited by ~58
  21. G. Litjens, N. Karssemeijer and H. Huisman, "A multi-atlas approach for prostate segmentation in MRI", MICCAI} {W}orkshop: {P}rostate {C}ancer {I}maging: The {PROMISE12} Prostate Segmentation Challenge, 2012. Abstract
  22. G. Litjens, P. Vos, J. Barentsz, N. Karssemeijer and H. Huisman, "Automatic Computer Aided Detection of Abnormalities in Multi-Parametric Prostate MRI", Medical Imaging, 2011;7963(1). Abstract DOI Cited by ~42
  23. W. van de Ven, G. Litjens, J. Barentsz, T. Hambrock and H. Huisman, "Required accuracy of MR-US registration for prostate biopsies", P}rostate {C}ancer {I}maging. {I}mage {A}nalysis and {I}mage-{G}uided {I}nterventions, 2011;6963:92-99. Abstract DOI Cited by ~8
  24. G. Litjens, L. Hogeweg, A. Schilham, P. de Jong, M. Viergever and B. van Ginneken, "Simulation of nodules and diffuse infiltrates in chest radiographs using CT templates", Medical Image Computing and Computer-Assisted Intervention, 2010;6362:396-403. Abstract DOI PMID Cited by ~3
  25. H. Huisman, P. Vos, G. Litjens, T. Hambrock and J. Barentsz, "Computer aided detection of prostate cancer using t2w, DWI and DCE-MRI: methods and clinical applications", MICCAI} {W}orkshop: {P}rostate {C}ancer {I}maging: {C}omputer {A}ided {D}iagnosis, {P}rognosis, and {I}ntervention, 2010. Abstract
  26. G. Litjens, M. Heisen, J. Buurman and B. ter Romeny, "Pharmacokinetic models in clinical practice: what model to use for DCE-MRI of the breast?", IEEE International Symposium on Biomedical Imaging, 2010:185-188. Abstract DOI Cited by ~11
  27. P. Snoeren, G. Litjens, B. van Ginneken and N. Karssemeijer, "Training a Computer Aided Detection System with Simulated Lung Nodules in Chest Radiographs", The Third International Workshop on Pulmonary Image Analysis, 2010:139-149. Abstract Cited by ~9

Abstracts

  1. 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
  2. H. Pinckaers and G. Litjens, "Training convolutional neural networks with megapixel images", Medical Imaging with Deep Learning, 2018. Abstract arXiv
  3. M. Hermsen, T. de Bel, M. van de Warenburg, J. Knuiman, E. Steenbergen, G. Litjens, B. Smeets, L. Hilbrands and J. van der Laak, "Automatic segmentation of histopathological slides from renal allograft biopsies using artificial intelligence", Dutch Federation of Nephrology (NfN) Fall Symposium, 2017. Abstract
  4. G. Litjens, N. Karssemeijer, J. Barentsz and H. Huisman, "Computer-aided Detection of Prostate Cancer in Multi-parametric Magnetic Resonance Imaging", Annual Meeting of the Radiological Society of North America, 2014. Abstract
  5. E. Vos, T. Kobus, G. Litjens, T. Hambrock, C. - van de Kaa, M. Maas and T. Scheenen, "Multiparametric MR imaging for the assessment of prostate cancer aggressiveness at 3 Tesla", Annual Meeting of the International Society for Magnetic Resonance in Medicine, 2014. Abstract
  6. M. Maas, M. Koopman, G. Litjens, A. Wright, K. Selnas, I. Gribbestad, M. Haider, K. Macura, D. Margolis, B. Kiefer, J. Fütterer and T. Scheenen, "Prostate Cancer localization with a Multiparametric MR Approach (PCaMAP): initial results of a multi-center study", Annual Meeting of the International Society for Magnetic Resonance in Medicine, 2013. Abstract Cited by ~2
  7. G. Litjens, J. Barentsz, N. Karssemeijer and H. Huisman, "Initial prospective evaluation of the prostate imaging reporting and data standard (PI-RADS): Can it reduce unnecessary MR guided biopsies?", Annual Meeting of the Radiological Society of North America, 2013. Abstract
  8. G. Litjens, J. Barentsz, N. Karssemeijer and H. Huisman, "Computerized characterization of central gland lesions using texture and relaxation features from T2-weighted prostate MRI", Annual Meeting of the Radiological Society of North America, 2012. Abstract
  9. E. Vos, G. Litjens, T. Kobus, T. Hambrock, C. van de Hulsbergen Kaa, H. Huisman and T. Scheenen, "Dynamic contrast enhanced MR imaging for the assessment of prostate cancer aggressiveness at 3T", Annual Meeting of the International Society for Magnetic Resonance in Medicine, 2012. Abstract
  10. G. Litjens, J. Barentsz, N. Karssemeijer and H. Huisman, "Zone-specific Automatic Computer-aided Detection of Prostate Cancer in MRI", Annual Meeting of the Radiological Society of North America, 2011. Abstract
  11. M. Schouten, K. Nagel, T. Hambrock, C. Hoeks, G. Litjens, J. Barentsz and J. Fütterer, "Differentiation of Normal Prostate Tissue, Prostatitis, and Prostate Cancer: Correlation between Diffusion-weighted Imaging and MR-guided Biopsy", Annual Meeting of the Radiological Society of North America, 2011. Abstract
  12. O. Debats, T. Hambrock, G. Litjens, H. Huisman and J. Barentsz, "Detection of Lymph Node Metastases with Ferumoxtran-10 vs Ferumoxytol", Annual Meeting of the Radiological Society of North America, 2011. Abstract
  13. G. Litjens, M. Heisen, J. Buurman, A. Wood, M. Medved, G. Karczmar and B. Haar-Romeny, "T1 Quantification: Variable Flip Angle Method vs Use of Reference Phantom", Annual Meeting of the Radiological Society of North America, 2009. Abstract

PhD theses

  1. B. Bejnordi, "Histopathological diagnosis of breast cancer using machine learning", 2017. Abstract Url

Master theses

  1. G. Litjens, "Pharmacokinetic modeling in breast cancer MRI", 2009. Abstract