Open AccessGAU-Net: U-Net Based on Global Attention Mechanism for brain tumor segmentation
Author(s) -
Xiuling Gan,
Lidan Wang,
Qi Chen,
Yongjie Ge,
Shukai Duan
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1861/1/012041
Subject(s) - computer science , net (polyhedron) , segmentation , artificial intelligence , residual , convolution (computer science) , pattern recognition (psychology) , inference , convolutional neural network , feature (linguistics) , pixel , algorithm , artificial neural network , mathematics , linguistics , philosophy , geometry
Deep learning has shown great advantages in biomedical image segmentation. The classic model U-Net uses a stacked encoding-decoding structure of convolution operations for feature extraction and pixel-level classification. The stacking of convolutional layers can expand the receptive field, but it is still a local operation and cannot capture long-distance dependence. Therefore, in this work, we propose a Global Attention Mechanism that combines channel attention module and spatial attention module and integrates different convolutions in it. Besides, we design a residual module for the traditional up and down sampling blocks. And finally, we combine them with U-Net to propose a new global attention network GAU-Net. We perform experiments on the dataset BraTS2018. Our model has increased the mIoU from 0.65 to 0.75 with only 5.4% of U-Net parameters. At the same time, the inference time is also significantly shortened with relatively good performance.