Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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F. Navarro, S. Conjeti, F. Tombari, N. Navab
Leveraging Web Data for Skin Lesion Classification
Proceedings of the Bildverarbeitung für die Medizin 2019 (bib)

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. Furthermore, a small-size and well-annotated data-set can not guarantee the generalizability to unseen samples.
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