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SEA-NET: SQUEEZE-AND-EXCITATION ATTENTION NET FOR DIABETIC RETINOPATHY GRADING

Citation Author(s):
Ziyuan Zhao, Kartik Chopra, Zeng Zeng, Xiaoli Li
Submitted by:
ZIYUAN ZHAO
Last updated:
20 January 2021 - 7:51am
Document Type:
Presentation Slides
Document Year:
2020
Event:
Presenters Name:
Ziyuan Zhao
Paper Code:
2630

Abstract 

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 intra-class variability. Experimental results have shown the effectiveness of the proposed architecture.

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Dataset Files

Presentation_ICIP.pdf

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