Abstract
Vision-threatening pathological myopia presents several lesions affecting various retinal anatomical structures. Detection approaches, however, either focus on one anatomical feature or are not intentional. This study uses hypergraph learning to modulate delineated retinal anatomical features from fundus images and capitalize on hidden associations between them. Experiments are conducted to assess prediction performance when targeting a particular anatomical trait versus using a mixture of select anatomical features, and in comparison to a ResNet34-based convolutional neural network classifier. Results indicate better prediction with hypergraph learning on a mix of the delineated features (F1 score \(89.75\%\), AUC score \(95.39\%\)). A choroid tessellation segmentation method is also included.
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Acknowledgements
This work was supported in part by Science, Technology, Innovation Commission of Shenzhen Municipality (JSGG20191129110812708; JSGG20200225150707332; WDZC20200820173710001; JCYJ20190809180003689), National Natural Science Foundation of China (31970752), Shenzhen Bay Laboratory Open Funding (SZBL2020090501004), China Postdoctoral Science Foundation (2020M680023), and General Administration of Customs of the People’s Republic of China (2021HK007).
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Githinji, B. et al. (2022). Multidimensional Hypergraph on Delineated Retinal Features for Pathological Myopia Task. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13432. Springer, Cham. https://doi.org/10.1007/978-3-031-16434-7_53
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