论文标题
睫毛：利用域移动以通过自行车来提高网络学习
EyeLoveGAN: Exploiting domain-shifts to boost network learning with cycleGANs
论文作者
论文摘要
本文介绍了我们对2020年避难挑战的贡献。挑战包括基于视网膜图像数据集的三个任务：视盘和杯子的分割，青光眼的分类以及Fovea的定位。我们建议针对这三个任务采用卷积神经网络。使用U-NET进行分割，分类是通过预先训练的InceptionV3网络进行的，并通过使用堆叠的小玻璃进行热图预测来进行中央凹检测。挑战数据集包含来自三个不同数据源的图像。为了提高性能，使用自行车范围来在数据源之间创建域移动。这些自行车手将图像跨域移动，从而创建可用于训练的人造图像。
This paper presents our contribution to the REFUGE challenge 2020. The challenge consisted of three tasks based on a dataset of retinal images: Segmentation of optic disc and cup, classification of glaucoma, and localization of fovea. We propose employing convolutional neural networks for all three tasks. Segmentation is performed using a U-Net, classification is performed by a pre-trained InceptionV3 network, and fovea detection is performed by employing stacked hour-glass for heatmap prediction. The challenge dataset contains images from three different data sources. To enhance performance, cycleGANs were utilized to create a domain-shift between the data sources. These cycleGANs move images across domains, thus creating artificial images which can be used for training.
