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In Klinische Monatsblatter fur Augenheilkunde

The use of deep neural networks ("deep learning") creates new possibilities in digital image processing. This approach has been widely applied and successfully used for the evaluation of image data in ophthalmology. In this article, the methodological approach of deep learning is examined and compared to the classical approach for digital image processing. The differences between the approaches are discussed and the increasingly important role of training data for model generation is explained. Furthermore, the approach of transfer learning for deep learning is presented with a representative data set from the field of corneal confocal microscopy. In this context, the advantages of the method and the specific problems when dealing with medical microscope data will be discussed.

Bartschat Andreas, Allgeier Stephan, Bohn Sebastian, Scherr Tim, Blessing Denis, Reichert Klaus-Martin, Reischl Markus, Stachs Oliver, Koehler Bernd, Mikut Ralf