Abstract：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.
张墺琦, 亢宇鑫, 武卓越, 崔磊, 卜起荣. 基于多尺度特征和注意力机制的肝脏组织病理图像语义分割网络[J]. 模式识别与人工智能, 2021, 34(4): 375-384.
ZHANG Aoqi, KANG Yuxin, WU Zhuoyue, CUI Lei, BU Qirong. Semantic Segmentation Network of Pathological Images of Liver Tissue Based on Multi-scale Feature and Attention Mechanism. , 2021, 34(4): 375-384.
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2021, Vol. 34 
