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Abstract: Leveraging Web Data for Skin Lesion Classification

  • Fernando NavarroEmail author
  • Sailesh Conjeti
  • Federico Tombari
  • Nassir Navab
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

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.

Literatur

  1. 1.
    Navarro F, Conjeti S, Tombari F, et al. Webly supervised learning for skin lesion classification. Proc MICCAI. 2018; p. 398–406.Google Scholar
  2. 2.
    Chen X, Gupta A. Webly supervised learning of convolutional networks. Proc CVPR. 2015; p. 1431–1439.Google Scholar
  3. 3.
    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.Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019

Authors and Affiliations

  • Fernando Navarro
    • 1
    Email author
  • Sailesh Conjeti
    • 2
  • Federico Tombari
    • 1
  • Nassir Navab
    • 1
    • 3
  1. 1.Computer Aided Medical ProceduresTechnische Universität MünchenMünchenDeutschland
  2. 2.Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)BonnDeutschland
  3. 3.Computer Aided Medical ProceduresJohns Hopkins UniversityBaltimoreUSA

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