  Glaucoma is a severe blinding disease, for which automatic detection methods
are urgently needed to alleviate the scarcity of ophthalmologists. Many works
have proposed to employ deep learning methods that involve the segmentation of
optic disc and cup for glaucoma detection, in which the segmentation process is
often considered merely as an upstream sub-task. The relationship between
fundus images and segmentation masks in terms of joint decision-making in
glaucoma assessment is rarely explored. We propose a novel segmentation-based
information extraction and amalgamation method for the task of glaucoma
detection, which leverages the robustness of segmentation masks without
disregarding the rich information in the original fundus images. Experimental
results on both private and public datasets demonstrate that our proposed
method outperforms all models that utilize solely either fundus images or
masks.
