Deep-Learning-with-PyTorch

366 CHAPTER 13 Using segmentation to find suspected nodules
harder to encode and therefore reconstruct. To address this, the U-Net authors added
the skip connections we see at the center of the figure. We first touched on skip connections
in chapter 8, although they are employed differently here than in the ResNet
architecture. In U-Net, skip connections short-circuit inputs along the downsampling
path into the corresponding layers in the upsampling path. These layers receive as input
both the upsampled results of the wide receptive field layers from lower in the U as well
as the output of the earlier fine detail layers via the “copy and crop” bridge connections.
This is the key innovation behind U-Net (which, interestingly, predated ResNet).
All of this means those final detail layers are operating with the best of both worlds.
They’ve got both information about the larger context surrounding the immediate
area and fine detail data from the first set of full-resolution layers.
The “conv 1x1” layer at far right, in the head of the network, changes the number
of channels from 64 to 2 (the original paper had 2 output channels; we have 1 in our
case). This is somewhat akin to the fully connected layer we used in our classification
network, but per-pixel, channel-wise: it’s a way to convert from the number of filters
used in the last upsampling step to the number of output classes needed.
13.4 Updating the model for segmentation
It’s time to move through step 2A in figure 13.8. We’ve had enough theory about
segmentation and history about U-Net; now we want to update our code, starting with
the model. Instead of just outputting a binary classification that gives us a single output
of true or false, we integrate a U-Net to get to a model that’s capable of outputting a
1. Segmentation
UNet
2. Update:
2a. Model
T/F
2b. Datasett
3. Results
2c. Training
Figure 13.8 The outline of this chapter, with a focus on the changes needed for our
segmentation model