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- W3046874920 abstract "Retinal image segmentation plays an important role in automatic disease diagnosis. This task is very challenging because the complex structure and texture information are mixed in a retinal image, and distinguishing the information is difficult. Existing methods handle texture and structure jointly, which may lead biased models toward recognizing textures and thus results in inferior segmentation performance. To address it, we propose a segmentation strategy that seeks to separate structure and texture components and significantly improve the performance. To this end, we design a structure-texture demixing network (STD-Net) that can process structures and textures differently and better. Extensive experiments on two retinal image segmentation tasks (i.e., blood vessel segmentation, optic disc and cup segmentation) demonstrate the effectiveness of the proposed method." @default.
- W3046874920 created "2020-08-07" @default.
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- W3046874920 date "2020-07-15" @default.
- W3046874920 modified "2023-09-26" @default.
- W3046874920 title "Retinal Image Segmentation with a Structure-Texture Demixing Network" @default.
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- W3046874920 doi "https://doi.org/10.48550/arxiv.2008.00817" @default.
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