U-Net论文阅读（逐句翻译+精读）

U-Net最初是一个用于二维图像分割的卷积神经网络，分别赢得了ISBI 2015细胞追踪挑战赛和龋齿检测挑战赛的冠军. U-net是基于全卷积网络拓展和修改而来，网络由两部分组成：一个收缩路径(contracting path)来获取上下文信息以及一个对称的扩张路径(expanding path）用以精确定位。下面就来精读一下这篇论文吧~

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1. Abstract

There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window
convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Moreover, the network is fast. Segmentation
of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net

• 为了更有效的利用标注数据，我们使用了数据增强的方法(data augmentation)。
• 我们的网络由两部分组成：一个收缩路径(contracting path)来获取上下文信息以及一个对称的扩张路径(expanding path)用以精确定位。

这种网络可以从很少的图像中进行端到端的训练。
这个网络非常的快

2. Introduction

In the last two years, deep convolutional networks have outperformed the state of the art in ma