Zusammenfassung
The success of deep learning is mainly based on the assumption that for the given application, there is access to a large amount of annotated data. In medical imaging applications, having access to a big-well-annotated data-set is restrictive, time-consuming and costly to obtain. Although diverse techniques as data augmentation can be leveraged to increase the size and variability within the data-set, the representativeness of the training set is still limited by the number of available samples.
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Navarro F, Conjeti S, Tombari F, et al. Webly supervised learning for skin lesion classification. Proc MICCAI. 2018; p. 398–406.
Chen X, Gupta A. Webly supervised learning of convolutional networks. Proc CVPR. 2015; p. 1431–1439.
Patrini G, Rozza A, Menon AK, et al. Making deep neural networks robust to label noise: a loss correction approach. Proc CVPR. 2017; p. 2233–2241.
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© 2019 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Navarro, F., Conjeti, S., Tombari, F., Navab, N. (2019). Abstract: Leveraging Web Data for Skin Lesion Classification. In: Handels, H., Deserno, T., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2019. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-25326-4_44
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DOI: https://doi.org/10.1007/978-3-658-25326-4_44
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