Medical Image Segmentation via Triplet Interactive Attention Network

被引：0
|
作者：
Gao C. [1 ]
Ye H. [1 ]
Cao F. [1 ]
机构：
[1] Department of Applied Mathematics, College of Sciences, China Jiliang University, Hangzhou
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2021年 / 34卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Attention mechanism; Class imbalance; Deep learning; Semantic segmentation;
D O I：
10.16451/j.cnki.issn1003-6059.202105002
中图分类号：
学科分类号：
摘要：
Deep learning produces advantages in solving class imbalance due to its powerful ability to extract features. However, its segmentation accuracy and efficiency can still be improved. A medical image segmentation algorithm via triplet interactive attention network is proposed in this paper. A triplet interactive attention module is designed and embedded into the feature extraction process. The module is focused on features in the channel and spatial dimensions jointly, capturing cross-dimensional interactive information. Thus, important features are in focus and target locations are highlighted. Moreover, pixel position-aware loss is employed to further mitigate the impact of class imbalance. Experiments on medical image datasets show that the proposed method yields better performance. © 2021, Science Press. All right reserved.
引用
收藏
页码：398 / 406
页数：8
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