@INPROCEEDINGS{9191345, author={Z. {Zhao} and K. {Chopra} and Z. {Zeng} and X. {Li}}, booktitle={2020 IEEE International Conference on Image Processing (ICIP)}, title={Sea-Net: Squeeze-And-Excitation Attention Net For Diabetic Retinopathy Grading}, year={2020}, volume={}, number={}, pages={2496-2500},}
Abstract:
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.
License type:
http://creativecommons.org/licenses/by-nc-nd/4.0/
Funding Info:
The work was supported by Singapore-China NRF-NSFC Grant (Grant No. NRF2016NRF-NSFC001-111).