Abstract
In the last years, deep convolutional networks have outperformed the state of the art in many visual recognition tasks. A central challenge for its wide adoption in the bio-medical imaging field is the limited amount of annotated training images. In this talk, I will present our u-net for biomedical image segmentation. The architecture consists of an analysis path and a synthesis path with additional shortcut-connections.
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Ronneberger, O. (2017). Invited Talk: U-Net Convolutional Networks for Biomedical Image Segmentation. In: Maier-Hein, geb. Fritzsche, K., Deserno, geb. Lehmann, T., Handels, H., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2017. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54345-0_3
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DOI: https://doi.org/10.1007/978-3-662-54345-0_3
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Publisher Name: Springer Vieweg, Berlin, Heidelberg
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Online ISBN: 978-3-662-54345-0
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