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arXiv · 2010.15344

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

Ziyuan Zhao·Kartik Chopra·Zeng Zeng·Xiaoli Li
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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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Ziyuan Zhao, Kartik Chopra, Zeng Zeng, Xiaoli Li. 2020-10-29. Sea-Net: Squeeze-And-Excitation Attention Net For Diabetic Retinopathy Grading. https://doi.org/10.1109/icip40778.2020.9191345

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