DPA‐UNet rectal cancer image segmentation based on visual attention

Author:

Wang Yuqian12ORCID,Ma JianWei1,Sergey Axyonov2,Zang Shaofei1,Zhang Miao1

Affiliation:

1. The School of Information Engineering Henan University of Science and Technology Luoyang China

2. Department of Information Technology Tomsk Polytechnic University Tomsk Russia

Abstract

SummaryLow segmentation accuracy and background noise exisit in rectal cancer lesion segmentation. To segment colorectal tumors in CT images accurately, we propose an improved U‐Net network for colorectal tumor image segmentation dilated‐pyramid‐attention U‐Net (DPA‐UNet). In this paper, we use the U‐Net network and combine techniques such as dilated convolution, weighted feature pyramid structure (W‐FPN), and convolutional block attention module (CBAM) mechanism. Firstly, CBAM and W‐FPN are combined to extract dense pixel‐level features for pixel labeling. Secondly, after the third network output layer, three serially dilated depth‐separable dilated convolutional layers with dilation rates of 1, 2, and 4, are added respectively to expand the feature map receptive field. Finally, the DPA‐UNet model is compared and analyzed with other new network structures. The experimental results show that DPA‐UNet achieves automatic segmentation of the colorectal cancer image region of interest (ROI).

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. FAANet: Feature-Augmented Attention Network for Surface Defect Detection of Metal Workpieces;Communications in Computer and Information Science;2023-11-05

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