GAU-Net: U-Net Based on Global Attention Mechanism for brain tumor segmentation. Issue 1 (March 2021)

• Record Type: Journal Article
• Authors: Gan, Xiuling; Wang, Lidan; Chen, Qi; Ge, Yongjie; Duan, Shukai
• Is Part Of: Journal of physics. Volume 1861:Issue 1(2021)
• Publication Date: 2021-03
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1861/1/012041
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: 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.