Deep-Learning-with-PyTorch

364 CHAPTER 13 Using segmentation to find suspected nodules
to use a technique called upsampling, which takes an image of a given resolution and
produces an image of a higher resolution. Upsampling at its simplest just means
replacing each pixel with an N × N block of pixels, each with the same value as the
original input pixel. The possibilities only get more complex from there, with options
like linear interpolation and learned deconvolution.
13.3.1 The U-Net architecture
Before we end up diving down a rabbit hole of possible upsampling algorithms, let’s
get back to our goal for the chapter. Per figure 13.6, step 1 is to get familiar with a
foundational segmentation algorithm called U-Net.
The U-Net architecture is a design for a neural network that can produce pixelwise
output and that was invented for segmentation. As you can see from the highlight
in figure 13.6, a diagram of the U-Net architecture looks a bit like the letter U, which
explains the origins of the name. We also immediately see that it is quite a bit more
complicated than the mostly sequential structure of the classifiers we are familiar with.
We’ll see a more detailed version of the U-Net architecture shortly, in figure 13.7, and
learn exactly what each of those components is doing. Once we understand the model
architecture, we can work on training one to solve our segmentation task.
1. Segmentation
UNet
2. Update:
2a. Model
T/F
2b. Dataset
t
3. Results
2c. Training
Figure 13.6
The new model architecture for segmentation, that we will be working with
The U-Net architecture shown in figure 13.7 was an early breakthrough for image segmentation.
Let’s take a look and then walk through the architecture.
In this diagram, the boxes represent intermediate results and the arrows represent
operations between them. The U-shape of the architecture comes from the multiple