Z. Zhao, K. Chopra, Z. Zeng and X. Li, "Sea-Net: Squeeze-And-Excitation Attention Net For Diabetic Retinopathy Grading," 2020 IEEE International Conference on Image Processing (ICIP), 2020, pp. 2496-2500, doi: 10.1109/ICIP40778.2020.9191345.
Diabetes is one of the most common disease in individuals. 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 intraclass variability. Experimental results have shown the effectiveness of the proposed architecture.
This research / project is supported by the The National Research Foundation of Singapore (NRF) and The National Natural Science Foundation of China (NSFC) - Singapore-China NRF-NSFC Grant
Grant Reference no. : NRF2016NRF-NSFC001-111