2018
DOI: 10.1007/978-3-030-00889-5_1
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Abstract: 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 feature maps from the decod… Show more

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Cited by 3,475 publications
(1,756 citation statements)
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References 13 publications
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“…RefineNet [70] improves the combination of upsampled representations and the representations of the same resolution copied from the downsample process. Other works include: light upsample process [5], [19], [72], [124], possibly with dilated convolutions used in the backbone [47], [69], [91]; light downsample and heavy upsample processes [115], recombinator networks [40]; improving skip connections with more or complicated convolutional units [48], [89], [143], as well as sending information from low-resolution skip connections to highresolution skip connections [151] or exchanging information between them [34]; studying the details of the upsample process [120]; combining multi-scale pyramid representations [18], [125]; stacking multiple DeconvNets/U-Nets/Hourglass [31], [122] with dense connections [110].…”
Section: Related Workmentioning
confidence: 99%
“…RefineNet [70] improves the combination of upsampled representations and the representations of the same resolution copied from the downsample process. Other works include: light upsample process [5], [19], [72], [124], possibly with dilated convolutions used in the backbone [47], [69], [91]; light downsample and heavy upsample processes [115], recombinator networks [40]; improving skip connections with more or complicated convolutional units [48], [89], [143], as well as sending information from low-resolution skip connections to highresolution skip connections [151] or exchanging information between them [34]; studying the details of the upsample process [120]; combining multi-scale pyramid representations [18], [125]; stacking multiple DeconvNets/U-Nets/Hourglass [31], [122] with dense connections [110].…”
Section: Related Workmentioning
confidence: 99%
“…Ronneberger et al [5] modified and extended the FCN architecture to an U-Net architecture. There are various modification and extension based on U-Net architecture [4], [6], [17], [19]- [22] to achieve better segmentation results on both natural images and biomedical images.…”
Section: Related Workmentioning
confidence: 99%
“…The selection of a deep learning or convolutional neural network model is the most important step in a medical image segmentation pipeline. There is a variety of model architectures and each has different strengths and weaknesses [7,8,10,11,[23][24][25][26][27][28][29].…”
Section: Model Architecturementioning
confidence: 99%