U-net：Convolutional networks for biomedical image segmentation

U-net：Convolutional networks for biomedical image segmentation

参考论文：《U-net: Convolutional networks for biomedical image segmentation》
https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/

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| max pooling | stride=2 |
| others | stride=1 (unpaddied) |

• based on Fully Convolutional Network
• contracting path; expansive path;
• The desired output include localization
• The upsampling part we have also a large number of feature channels -> higher resolution
• CHALLENGE: The separation of touching objects of the same class.
• upsample and downsample
  • At each downsampling setp we double the number of feature channels
  • At each upsampling step we halves the number of feature channels
• each 3x3 convolution followed by a ReLU
• SGD
• high momentum: 0.99;

最后输出，有K个channel，每个Channel代表一个类别。

对于ground truth，相同类别存在相连的情况：

(在处理同类别连接边界问题和groud truth权重计算方面，有些不理解）