论文标题
回归和学习以像素的注意力为视网膜眼底青光眼分割和检测
Regression and Learning with Pixel-wise Attention for Retinal Fundus Glaucoma Segmentation and Detection
论文作者
论文摘要
通过眼科医生观察视网膜眼底图像是青光眼的主要诊断方法。但是，仅通过手动观察，尤其是在青光眼的早期阶段，很难区分病变的特征。在本文中，我们介绍了两种基于深度学习的自动化算法，用于青光眼检测，视盘和杯子分段。我们利用注意力机制来学习像素的特征，以进行准确的预测。特别是，我们提出了两个卷积神经网络，可以专注于学习各种像素级的特征。此外，我们制定了几种关注策略，以指导网络学习对预测准确性产生重大影响的重要功能。我们在验证数据集上评估了我们的方法，并且所提出的两种任务的解决方案都可以取得令人印象深刻的结果，并且表现优于当前最新方法。 \ textIt {该代码可在\ url {https://github.com/cswin/rlpa}}上获得。
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}}.
