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A comprehensive study of age-related macular degeneration detection

  • 1195: Deep Learning for Multimedia Signal Processing and Applications
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Abstract

Age-related macular degeneration (AMD) is an illness involving the degeneration of the macula of the retina. Fundus photography is the most affordable and convenient way to monitor individuals, in which AMD symptoms segmentation is necessary to assist clinical diagnosis. This study conducted a large number of experimental discussions on the annotation quality and symptoms categories to find a reliable learning strategy, and then applied it to early detection of AMD. Specifically, we discuss the inference of the representational power of the deep neural network, loss function selection, the preprocessing scheme of annotation augmentation, and the annotation quality of the dataset on prediction performance. This paper verified that different learning strategies need to be selected for the AMD symptoms segmentation tasks with varying characteristics of database, which can be used as a reference for developing the related research in the future. On the other hand, we demonstrated that current medical datasets suffer from annotation quality uncertainty, leading to limited learning capabilities. In the future, it is necessary to develop methods to overcome the impact of datasets with poor annotation quality.

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Authors and Affiliations

  1. Institute of Data Science, National Cheng Kung University, Tainan, Taiwan

    Chih-Chung Hsu

  2. Department of Electrical Engineering, National United University, Miaoli, Taiwan

    Chia-Yen Lee, Cheng-Jhong Lin & Hung Yeh

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  1. Chih-Chung Hsu
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  2. Chia-Yen Lee
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  3. Cheng-Jhong Lin
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  4. Hung Yeh
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Correspondence to Chia-Yen Lee.

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Hsu, CC., Lee, CY., Lin, CJ. et al. A comprehensive study of age-related macular degeneration detection. Multimed Tools Appl 81, 11897–11916 (2022). https://doi.org/10.1007/s11042-021-11896-8

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  • DOI: https://doi.org/10.1007/s11042-021-11896-8

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