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From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge.

P. Bándi, O. Geessink, Q. Manson, M. 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. Çetin, E. Halici, H. Jackson, R. Chen, F. Both, J. Franke, H. Küsters-Vandevelde, W. Vreuls, P. Bult, B. van Ginneken, J. van der Laak, and G. Litjens. IEEE Trans. Med. Imaging, 38 (2): 550-560 (2019)

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Novel chromatin texture features for the classification of pap smears.B. Bejnordi, R. Moshavegh, K. Sujathan, P. Malm, E. Bengtsson, and A. Mehnert. Medical Imaging: Digital Pathology, volume 8676 of SPIE Proceedings, page 867608. SPIE, (2013)Automated Detection of DCIS in Whole-Slide H&E Stained Breast Histopathology Images.B. Bejnordi, M. Balkenhol, G. Litjens, R. Holland, P. Bult, N. Karssemeijer, and J. van der Laak. IEEE Trans. Med. Imaging, 35 (9): 2141-2150 (2016)Stain Specific Standardization of Whole-Slide Histopathological Images.B. Bejnordi, G. Litjens, N. Timofeeva, I. Otte-Höller, A. Homeyer, N. Karssemeijer, and J. van der Laak. IEEE Trans. Med. Imaging, 35 (2): 404-415 (2016)Whole Mastectomy Volume Reconstruction from 2D Radiographs and Its Mapping to Histology.T. Mertzanidou, J. Hipwell, S. Reis, B. Bejnordi, M. Hermsen, M. Dalmis, S. Vreemann, B. Platel, J. van der Laak, N. Karssemeijer and 3 other author(s). Digital Mammography / IWDM, volume 9699 of Lecture Notes in Computer Science, page 367-374. Springer, (2016)A Survey on Deep Learning in Medical Image Analysis.G. Litjens, T. Kooi, B. Bejnordi, A. Setio, F. Ciompi, M. Ghafoorian, J. van der Laak, B. van Ginneken, and C. Sánchez. CoRR, (2017)The importance of stain normalization in colorectal tissue classification with convolutional networks.F. Ciompi, O. Geessink, B. Bejnordi, G. de Souza, A. Baidoshvili, G. Litjens, B. van Ginneken, I. Nagtegaal, and J. van der Laak. CoRR, (2017)From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge.P. Bándi, O. Geessink, Q. Manson, M. Dijk, M. Balkenhol, M. Hermsen, B. Bejnordi, B. Lee, K. Paeng, A. Zhong and 26 other author(s). IEEE Trans. Med. Imaging, 38 (2): 550-560 (2019)Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images.B. Bejnordi, G. Zuidhof, M. Balkenhol, M. Hermsen, P. Bult, B. van Ginneken, N. Karssemeijer, G. Litjens, and J. van der Laak. CoRR, (2017)A multi-scale superpixel classification approach to the detection of regions of interest in whole slide histopathology images.B. Bejnordi, G. Litjens, M. Hermsen, N. Karssemeijer, and J. van der Laak. Medical Imaging: Digital Pathology, volume 9420 of SPIE Proceedings, page 94200H. SPIE, (2015)Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images.B. Bejnordi, J. Lin, B. Glass, M. Mullooly, G. Gierach, M. Sherman, N. Karssemeijer, J. van der Laak, and A. Beck. ISBI, page 929-932. IEEE, (2017)

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From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge.

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From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge.