A comprehensive study of age-related macular degeneration detection

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.