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

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Sea-Net: Squeeze-And-Excitation Attention Net For Diabetic Retinopathy Grading
Title:
Sea-Net: Squeeze-And-Excitation Attention Net For Diabetic Retinopathy Grading
Other Titles:
2020 IEEE International Conference on Image Processing (ICIP)
Publication URL:
Publication Date:
30 September 2020
Citation:
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.
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:
Publisher Copyright
Funding Info:
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
Description:
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2381-8549
ISBN:
978-1-7281-6395-6
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