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Lecture Notes in Computer Science
In this paper, we present UNet++, a new, more powerful architecture for medical image segmentation. Our architecture is essentially a deeply-supervised encoder-decoder network where the encoder and decoder sub-networks are connected through a series of nested, dense skip pathways. The re-designed skip pathways aim at reducing the semantic gap between the feature maps of the encoder and decoder sub-networks. We argue that the optimizer would deal with an easier learning task when the featuredoi:10.1007/978-3-030-00889-5_1 pmid:32613207 pmcid:PMC7329239 fatcat:tlcuw5okfngexos73kmhimkoya