U-Net Using Stacked Dilated Convolutions for Medical Image Segmentation
This paper proposes a novel U-Net variant using stacked dilated convolutions for medical image segmentation (SDU-Net). SDU-Net adopts the architecture of vanilla U-Net with modifications in the encoder and decoder operations (an operation indicates all the processing for feature maps of the same res...
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Main Authors | , , , , , , , , , , , , |
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Format | Journal Article |
Language | English |
Published |
07.04.2020
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Subjects | |
Online Access | Get full text |
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Summary: | This paper proposes a novel U-Net variant using stacked dilated convolutions
for medical image segmentation (SDU-Net). SDU-Net adopts the architecture of
vanilla U-Net with modifications in the encoder and decoder operations (an
operation indicates all the processing for feature maps of the same
resolution). Unlike vanilla U-Net which incorporates two standard convolutions
in each encoder/decoder operation, SDU-Net uses one standard convolution
followed by multiple dilated convolutions and concatenates all dilated
convolution outputs as input to the next operation. Experiments showed that
SDU-Net outperformed vanilla U-Net, attention U-Net (AttU-Net), and recurrent
residual U-Net (R2U-Net) in all four tested segmentation tasks while using
parameters around 40% of vanilla U-Net's, 17% of AttU-Net's, and 15% of
R2U-Net's. |
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DOI: | 10.48550/arxiv.2004.03466 |