Sea-Net: Squeeze-And-Excitation Attention Net For Diabetic Retinopathy Grading
This work addresses automatic grading of diabetic retinopathy for medical diagnosis, but it appears incremental as it builds on existing attention mechanisms.
The authors tackled the problem of grading diabetic retinopathy severity from retinal images by proposing SEA-Net, a deep learning architecture that alternates spatial and channel attention to improve classification, achieving effective results as shown in experiments.
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.