Sea-Net: Squeeze-And-Excitation Attention Net For Diabetic Retinopathy Grading

• URL: http://arxiv.org/abs/2010.15344v1
• Date: Thu, 29 Oct 2020 03:48:01 GMT
• Title: Sea-Net: Squeeze-And-Excitation Attention Net For Diabetic Retinopathy Grading
• Authors: Ziyuan Zhao, Kartik Chopra, Zeng Zeng, Xiaoli Li
• Abstract summary: Diabetes is one of the most common disease in individuals. Diabetic retinopathy (DR) is a complication of diabetes, which could lead to blindness. DR grading based on retinal images provides a great diagnostic and prognostic value for treatment planning.
• Score: 9.181677987146418
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diabetes is one of the most common disease in individuals. \textit{Diabetic retinopathy} (DR) is a complication of diabetes, which could lead to blindness. Automatic DR grading based on retinal images provides a great diagnostic and prognostic value for treatment planning. However, the subtle differences among severity levels make it difficult to capture important features using conventional methods. To alleviate the problems, a new deep learning architecture for robust DR grading is proposed, referred to as SEA-Net, in which, spatial attention and channel attention are alternatively carried out and boosted with each other, improving the classification performance. In addition, a hybrid loss function is proposed to further maximize the inter-class distance and reduce the intra-class variability. Experimental results have shown the effectiveness of the proposed architecture.

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