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A comparative study of semi- and self-supervised semantic segmentation of biomedical microscopy data
Autor:
Horlava, Nastassya, Mironenko, Alisa, Niehaus, Sebastian, Wagner, Sebastian, Roeder, Ingo, Scherf, Nico
In recent years, Convolutional Neural Networks (CNNs) have become the state-of-the-art method for biomedical image analysis. However, these networks are usually trained in a supervised manner, requiring large amounts of labelled training data. These
Externí odkaz:
http://arxiv.org/abs/2011.08076