INet: Convolutional Networks for Biomedical Image Segmentation

Encoder-decoder networks are state-of-the-art approaches to biomedical image segmentation, but have two problems: i.e., the widely used pooling operations may discard spatial information, and therefore low-level semantics are lost. Feature fusion methods can mitigate these problems but feature maps...

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Vydané v:	IEEE access Ročník 9; s. 16591 - 16603
Hlavní autori:	Weng, Weihao, Zhu, Xin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Biomedical image Biomedical imaging Coders Convolution convolutional networks encoder–decoder networks Feature extraction Feature maps Image segmentation Kernel Kernels Medical imaging semantic segmentation Semantics Spatial data Spatial resolution Tumors
ISSN:	2169-3536, 2169-3536
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