Segmentation on OCTA Image of Fundus in vivo based on Attention Module
Abstract
Diabetic retinopathy (DR) is one of the serious complication of diabetes, which will have a certain influence on vision and even lead to blindness. Early detection and treatment of DR is very important. With the increasing number of patients with retinal diseases, there is an urgent need for automatic diagnosis of retinal diseases. To address the problem of DR lesion segmentation in Optical Coherence Tomography fundus images, we propose a novel DR lesion segmentation method, namely pyramid map attention-based convolutional neural network (PACN). To learn the effective feature, we design the Bidirectional Pyramid Attention Module (BPAM). With the BPAM subnetwork, the receptive field can be increased and improve the performance of the model. The information of code and dataset can be viewed at URL https://17861318579.github.io/PACN. Experimental results demonstrate the effectiveness of our new method.
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March 2024
478 pages
ISBN:9798400716416
DOI:10.1145/3654823
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Published: 29 May 2024
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CACML 2024: 2024 3rd Asia Conference on Algorithms, Computing and Machine Learning
March 22 - 24, 2024
Shanghai, China
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