Semantic Segmentation Network of Pathological Images of Liver Tissue Based on Multi-scale Feature and Attention Mechanism

被引：0
作者：
Zhang A. [1 ]
Kang Y. [1 ]
Wu Z. [1 ]
Cui L. [1 ]
Bu Q. [1 ]
机构：
[1] School of Information Science and Technology, Northwest University, Xi'an
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2021年 / 34卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Attention mechanism; Extraction and fusion of multi-scale feature; Pathological image of liver tissue; Semantic segmentation;
D O I：
10.16451/j.cnki.issn1003-6059.202104010
中图分类号：
学科分类号：
摘要：
To address the problem of difficult segmentation and many voids in the transition regions of normal and abnormal tissues in liver histopathology images segmentation, a semantic segmentation network of pathological images of liver tissue based on multi-scale feature and attention mechanism is proposed. The fused multi-scale features are extracted in the encoder to improve the segmentation of the transition regions between normal and abnormal tissues. The attention mechanism is employed to model the correlation between spatial dimension and channel dimension to obtain the response of each pixel within each class as well as the dependency between channels, and the impact of many voids in liver histopathology images on the network learning is alleviated. Experiments demonstrate that the proposed network can segment the damaged regions of liver histopathology images more quickly and accurately. © 2021, Science Press. All right reserved.
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页码：375 / 384
页数：9
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