Abstract
Diabetic Retinopathy (DR) is a very common retinal disease in the world, which can affect vision and even cause blindness. Early diagnosis can effectively prevent the disease, or at least delay the progression of DR. However, most methods are based on regular single-view images, which would lack complete information of lesions. In this paper, a novel method is proposed to achieve DR classification using ultra-widefield images (UWF). The proposed network includes a dual-branch network, an efficient channel attention (ECA) module, a spatial attention (SA) module, and an atrous spatial pyramid pooling (ASPP) module. Specifically, the dual-branch network uses ResNet-34 model as the backbone. The ASPP module enlarges the receptive field to extract rich feature information by setting different dilated rates. To emphasize the useful information and suppress the useless information, the ECA and SA modules are utilized to extract important channel information and spatial information respectively. To reduce the parameters of the network, we use a global average pooling (GAP) layer to compress the features. The experimental results on the UWF images collected by a local hospital show that our model performs very well.
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Acknowledgements
This work was supported partly by National Natural Science Foundation of China (Nos. 61871274, 61801305 and 81571758), National Natural Science Foundation of Guangdong Province (No. 2020A1515010649 and No. 2019A1515111205), Guangdong Province Key Laboratory of Popular High Performance Computers (No. 2017B030314073), Guangdong Laboratory of Artificial-Intelligence and Cyber-Economics (SZ), the China Postdoctoral Science Foundation (2021M692196), Shenzhen Peacock Plan (Nos. KQTD2016053112051497 and KQTD2015033016104926), Shenzhen Key Basic Research Project (Nos. JCYJ20190808165209410, 20190808145011259, JCYJ20180507184647636, GJHZ20190822095414576 and JCYJ20170302153337765, JCYJ20170302150411789, JCYJ20170302142515949, GCZX2017040715180580, GJHZ20180418190529516, and JSGG20180507183215520), NTUT-SZU Joint Research Program (No. 2020003), Special Project in Key Areas of Ordinary Universities of Guangdong Province (No. 2019KZDZX1015), Shenzhen Key Medical Discipline Construction Fund (No. SZXK038), Shenzhen Fund for Guangdong Provincial High-level Clinical Key Speciaties (No. SZGSP014), Shenzhen-Hong Kong Co-financing Project (No. SGDX20190920110403741).
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Tian, Z. et al. (2021). Dual-Branch Attention Network and Atrous Spatial Pyramid Pooling for Diabetic Retinopathy Classification Using Ultra-Widefield Images. In: Fu, H., Garvin, M.K., MacGillivray, T., Xu, Y., Zheng, Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2021. Lecture Notes in Computer Science(), vol 12970. Springer, Cham. https://doi.org/10.1007/978-3-030-87000-3_13
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