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Observing retinal fundus images by an ophthalmologist is a major diagnosisapproach for glaucoma. However, it is still difficult to distinguish thefeatures of the lesion solely through manual observations, especially, inglaucoma early phase. In this paper, we present two deep learning-basedautomated algorithms for glaucoma detection and optic disc and cupsegmentation. We utilize the attention mechanism to learn pixel-wise featuresfor accurate prediction. In particular, we present two convolutional neuralnetworks that can focus on learning various pixel-wise level features. Inaddition, we develop several attention strategies to guide the networks tolearn the important features that have a major impact on prediction accuracy.We evaluate our methods on the validation dataset and The proposed both tasks'solutions can achieve impressive results and outperform currentstate-of-the-art methods. \textit{The code is available at\url{https://github.com/cswin/RLPA}}.

Regression and Learning with Pixel-wise Attention for Retinal Fundus Glaucoma Segmentation and Detection