比例(比率)
特征(语言学)
计算机科学
网(多面体)
维数(图论)
分割
人工智能
图像(数学)
图像融合
模式识别(心理学)
计算机视觉
数学
地图学
地理
语言学
哲学
纯数学
几何学
作者
Xu Shijie,Y Chen,Shukai Yang,Xiaoqian Zhang,Feng Sun
标识
DOI:10.1016/j.compbiomed.2024.108947
摘要
Recently, ViT and CNNs based on encoder-decoder architecture have become the dominant model in the field of medical image segmentation. However, there are some deficiencies for each of them: (1) It is difficult for CNNs to capture the interaction between two locations with consideration of the longer distance. (2) ViT cannot acquire the interaction of local context information and carries high computational complexity. To optimize the above deficiencies, we propose a new network for medical image segmentation, which is called FCSU-Net. FCSU-Net uses the proposed collaborative fusion of multi-scale feature block that enables the network to obtain more abundant and more accurate features. In addition, FCSU-Net fuses full-scale feature information through the FFF (Full-scale Feature Fusion) structure instead of simple skip connections, and establishes long-range dependencies on multiple dimensions through the CS (Cross-dimension Self-attention) mechanism. Meantime, every dimension is complementary to each other. Also, CS mechanism has the advantage of convolutions capturing local contextual weights. Finally, FCSU-Net is validated on several datasets, and the results show that FCSU-Net not only has a relatively small number of parameters, but also has a leading segmentation performance.
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