作者：Bo Wang1,2, Shuang Qiu2, and Huiguang He1,2,3
目的：Retinal Vessel Segmentation is an essential step for the early diagnosis of eye-related diseases, such as diabetes and hypertension. Segmentation of blood vessels requires both sizeable receptive field and rich spatial information.
方法：Dual Encoding U-Net (DEU-Net), 空间information和上下文information
该结构图outputpatches竟然和input一样。
- Spatial Path，连续四个卷积，用了大stride：7，12，9，6.参考了Large kernel matters-improve semantic segmentation by global convolutional network.， CVPR2017。
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Context Path，就是inception block，google提出的
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Attention Skip Module，最简单的attention方式处理
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Feature Fusion Module，这个方式我看到过，不知道为什么叫做feature fusion，其实连结处就是和attention residual for image classification那篇文章一样.
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Multiscale Predict Module，这个模块没看到过，主要是pixel shuffle（参考CVPR2016 Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network）这个操作。
试验结果：略