Zobrazeno 1 - 2
of 2
pro vyhledávání: '"Melih cetin"'
Autor:
Jörg Franke, Keisuke Fukuta, Hao Chen, Willem Vreuls, Aoxiao Zhong, Farhad Ghazvinian Zanjani, Svitlana Zinger, Richard J. Chen, Hunter Jackson, Fabian Both, Heidi V.N. Küsters-Vandevelde, Daisuke Komura, Babak Ehteshami Bejnordi, Marcory C. R. F. van Dijk, Bram van Ginneken, Eren Halici, Ludwig Jacobsson, Vlado Ovtcharov, Quanzheng Li, Jeroen van der Laak, Peter Bult, Oscar Geessink, Melih cetin, Shaoqun Zeng, Geert Litjens, Martin Hedlund, Anders Bjorholm Dahl, Byungjae Lee, Péter Bándi, Huangjing Lin, Jeppe Thagaard, Quirine F. Manson, Meyke Hermsen, Shenghua Cheng, Kyunghyun Paeng, Maschenka Balkenhol
Publikováno v:
Bandi, P, Geessink, O, Manson, Q, van Dijk, M, Balkenhol, M, Hermsen, M, Bejnordi, B E, Lee, B, Paeng, K, Zhong, A, Li, Q, Zanjani, F G, Zinger, S, Fukuta, K, Komura, D, Ovtcharov, V, Cheng, S, Zeng, S, Thagaard, J, Dahl, A B, Lin, H, Chen, H, Jacobsson, L, Hedlund, M, Cetin, M, Halici, E, Jackson, H, Chen, R, Both, F, Franke, J, Kusters-Vandevelde, H, Vreuls, W, Bult, P, van Ginneken, B, van der Laak, J & Litjens, G 2018, ' From detection of individual metastases to classification of lymph node status at the patient level: the CAMELYON17 challenge ', I E E E Transactions on Medical Imaging, vol. 38, no. 2, pp. 550-560 . https://doi.org/10.1109/TMI.2018.2867350
IEEE Transactions on Medical Imaging, 38, 2, pp. 550-560
IEEE Transactions on Medical Imaging, 38, 550-560
IEEE Transactions on Medical Imaging, 38(2):8447230, 550-560. Institute of Electrical and Electronics Engineers
IEEE Transactions on Medical Imaging, 38, 2, pp. 550-560
IEEE Transactions on Medical Imaging, 38, 550-560
IEEE Transactions on Medical Imaging, 38(2):8447230, 550-560. Institute of Electrical and Electronics Engineers
Automated detection of cancer metastases in lymph nodes has the potential to improve the assessment of prognosis for patients. To enable fair comparison between the algorithms for this purpose, we set up the CAMELYON17 challenge in conjunction with t
Publikováno v:
2010 IAPR Workshop on Pattern Recognition in Remote Sensing.
A method based on textural features and Adaboost for detecting buildings in satellite images is proposed. Several local textural features including mean and standard deviation of image intensity and gradient, Zernike moments, Circular-Mellin features