Regression and Learning with Pixel-wise Attention for Retinal Fundus Glaucoma Segmentation and Detection
This work addresses the challenge of early glaucoma diagnosis for ophthalmologists by providing automated tools to improve accuracy over manual observations.
The paper tackles automated glaucoma detection and optic disc/cup segmentation from retinal fundus images by using deep learning with pixel-wise attention mechanisms, achieving results that outperform current state-of-the-art methods.
Observing retinal fundus images by an ophthalmologist is a major diagnosis approach for glaucoma. However, it is still difficult to distinguish the features of the lesion solely through manual observations, especially, in glaucoma early phase. In this paper, we present two deep learning-based automated algorithms for glaucoma detection and optic disc and cup segmentation. We utilize the attention mechanism to learn pixel-wise features for accurate prediction. In particular, we present two convolutional neural networks that can focus on learning various pixel-wise level features. In addition, we develop several attention strategies to guide the networks to learn 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 current state-of-the-art methods. \textit{The code is available at \url{https://github.com/cswin/RLPA}}.