GANet: group attention network for diabetic retinopathy image segmentation

Lei Ye; Weifang Zhu; Shuanglang Feng; Xinjian Chen

doi:10.1117/12.2548310

10 March 2020 GANet: group attention network for diabetic retinopathy image segmentation

Lei Ye, Weifang Zhu, Shuanglang Feng, Xinjian Chen

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 1131307 (2020) https://doi.org/10.1117/12.2548310
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

The assistance of deep learning techniques for clinic doctors in disease analysis, diagnosis and treatment is becoming popular and popular. In this paper, we propose a U-shape architecture based Group Attention network (named as GANet) for symptom segmentation in fundus images with diabetic retinopathy, in which Channel Group Attention(CGA) module and Spatial Group Attention Upsampling (SGAU) module are designed. The CGA module can adaptively allocate resources based on the importance of the feature channels, which can enhance the flexibility of the network to handle different types of information. The original U-Net directly merges the high-level features and low-level features in decoder stage for semantic segmentation, and achieves good results. To increase the nonlinearity of the U-shape network and pay more attention to the lesion area, we propose a Spatial Group Attention Upsampling (SGAU) module. In summary, our main contributions include two aspects: (1) Based on the U-shape network, the CGA module and SGAU module are designed and applied, which can adaptively allocate the weight of channels and pay more attention to the lesion area, respectively. (2) Compared with the original U-Net, the Dice coefficients of the proposed network improves by nearly 2.96% for hard exudates segmentation and 2.89% for hemorrhage segmentation, respectively.

Conference Presentation

Citation Download Citation

Lei Ye, Weifang Zhu, Shuanglang Feng, and Xinjian Chen "GANet: group attention network for diabetic retinopathy image segmentation", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 1131307 (10 March 2020); https://doi.org/10.1117/12.2548310

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