Fully extracting feature correlation between and within stages for semantic segmentation

Yuan, Feiniu; Li, Kang; Wang, Chunmei; Shi, Jinting; Zhu, Yaowen

doi:10.1016/j.dsp.2022.103578

Cited by 10 publications

(3 citation statements)

References 11 publications

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“…To improve performance, complicated and powerful networks, such as VGG [21] and ResNet [22], have been proposed and used as the backbone structures of segmentation methods. Yuan et al [23] extracted feature correlation between and within encoding stages to propose a semantic segmentation method. Many U-shaped methods have been proposed by extending U-Net [8], which consists of an encoder and a decoder.…”

Section: Convolutional Neural Network-based Methodsmentioning

confidence: 99%

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A multiple gated boosting network for multi‐organ medical image segmentation

Yuan

Tang

Wang

et al. 2023

IET Image Processing

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Segmentations provide important clues for diagnosing diseases. U‐shaped neural networks with skip connections have become one of popular frameworks for medical image segmentation. Skip connections really reduce loss of spatial details caused by down‐sampling, but they cannot handle well semantic gaps between low‐ and high‐level features. It is quite challenging to accurately separate out long, narrow, and small organs from human bodies. To solve these problems, the authors propose a Multiple Gated Boosting Network (MGB‐Net). To boost spatial accuracy, the authors first adopt Gated Recurrent Units (GRU) to design multiple Gated Skip Connections (GSC) at different levels, which efficiently reduce the semantic gap between the shallow and deep features. The Update and Reset gates of GRUs enhance features beneficial to segmentation and suppress information adverse to final results in a recurrent way. To obtain more scale invariances, the authors propose a module of Multi‐scale Weighted Channel Attention (MWCA). The module first uses convolutions with different kernel sizes and group numbers to generate multi‐scale features, and then adopts learnable weights to emphasize the importance of each scale for capturing attention features. Blocks of Transformer Self‐Attention (TSA) are sequentially stacked to extract long‐range dependency features. To effectively fuse and boost the features of MWCA and TSA, the authors use GRUs again to propose a Gated Dual Attention module (GDA), which enhances beneficial features and suppresses adverse information in a gated learning way. Experiments show that the authors’ method achieves an average Dice coefficient of 80.66% on the Synapse multi‐organ segmentation dataset. The authors’ method outperforms the state‐of‐the‐art methods on medical images. In addition, the authors’ method achieves a Dice segmentation accuracy of 62.77% on difficult objects such as pancreas, significantly exceeding the current average accuracy, so multiple gated boosting (MGB) methods are reliably effective for improving the ability of feature representations. The authors’ code is publicly available at https://github.com/DAgalaxy/MGB-Net.

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Section: Convolutional Neural Network-based Methodsmentioning

confidence: 99%

“…Yuan et al. [23] extracted feature correlation between and within encoding stages to propose a semantic segmentation method. Many U‐shaped methods have been proposed by extending U‐Net [8], which consists of an encoder and a decoder.…”

Section: Related Workmentioning

confidence: 99%

A multiple gated boosting network for multi‐organ medical image segmentation

Yuan

Tang

Wang

et al. 2023

IET Image Processing

Self Cite

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“…As the important role of feature extraction, many algorithms have been proposed to extract better features [6,7]. Scale-invariant feature transform (SIFT) finds the key points in different scale-spaces and calculates the directions of the key points [8,9].…”

Section: Introductionmentioning

confidence: 99%

Global and Local Structure Network for Image Classification

2023

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Principal Component Analysis Network (PCANet) is a feature learning algorithm which is widely used in face recognition and object classification. However, original PCANet still has some shortages. One is that PCA algorithm only extracts features by considering the global structure. The other lies in that the original PCANet only employs one particular single layer convolutional results, which loses the information of other convolutional layers. In this paper, we propose a new simple and efficient convolutional neural network called global and local structure network (GLSNet) to address the problems. The network extracts the features both from the global structure and the local structure of the original data space. Specifically, a principal component analysis (PCA) convolutional layer which learns the filters by PCA algorithm is used to remove the noises and redundant information at the first stage. Then at the second stage, another PCA convolution is added to extract features by considering the global structure. As for the local structure, we use the neighborhood preserving embedding (NPE) algorithm to learn the convolutional filters. At the output stage, the global structure feature extracted by PCA convolution and the local structure feature extracted by NPE convolution is concatenated as a united feature. Furthermore, the first layer convolutional feature is also taken into consideration to obtain shallow-level information. Finally, these features are concatenated as a united feature, and a spatial pyramid pooling layer is followed to pool above the united features. To test the effectiveness of the proposed algorithm, the experiments on some image datasets, including three types: human face dataset, object dataset, and handprinted dataset, proceeded. And it performs better than the original PCANet and some improvement algorithms of PCANet, such as PLDANet, and MMPCANet.

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MFENet: Multitype fusion and enhancement network for detecting salient objects in RGB-T images

Zhou

Qian

et al. 2023

Digital Signal Processing

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Fully extracting feature correlation between and within stages for semantic segmentation

Cited by 10 publications

References 11 publications

A multiple gated boosting network for multi‐organ medical image segmentation

A multiple gated boosting network for multi‐organ medical image segmentation

Global and Local Structure Network for Image Classification

MFENet: Multitype fusion and enhancement network for detecting salient objects in RGB-T images

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