Deep-Learning-with-PyTorch

Towards the finish line
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14.1 Towards the finish line
We can get a hint of the work remaining by looking at figure 14.1. In step 3 (grouping)
we see that we still need to build the bridge between the segmentation model from
chapter 13 and the classifier from chapter 12 that will tell us whether what the segmentation
network found is, indeed, a nodule. On the right is step 5 (nodule analysis and
diagnosis), the last step to the overall goal: seeing whether a nodule is cancer. This is
another classification task; but to learn something in the process, we’ll take a fresh
angle at how to approach it by building on the nodule classifier we already have.
Step 1 (ch. 10):
Data Loading
STep 2 (ch. 13):
Segmentation
Step 4 (ch. 11+12):
ClaSsification
.MHD
.RAW
CT
Data
segmentation
model
Step 3 (ch. 14):
Grouping
candidate
Locations
[(I,R,C),
(I,R,C),
(I,R,C),
...
]
candidate
Sample
ClaSsification
model
[NEG,
p=0.1
POS,
p=0.9
NEG,
p=0.2
...
]
Step 5 (ch. 14):
Nodule analysis
and Diagnosis
MAL/BEN
p=0.9
Figure 14.1 Our end-to-end lung cancer detection project, with a focus on this chapter’s
topics: steps 3 and 5, grouping and nodule analysis
Of course, these brief descriptions and their simplified depiction in figure 14.1 leave
out a lot of detail. Let’s zoom in a little with figure 14.2 and see what we’ve got left to
accomplish.
As you can see, three important tasks remain. Each item in the following list corresponds
to a major line item from figure 14.2:
1 Generate nodule candidates. This is step 3 in the overall project. Three tasks go
into this step:
a Segmentation—The segmentation model from chapter 13 will predict if a
given pixel is of interest: if we suspect it is part of a nodule. This will be done
per 2D slice, and every 2D result will be stacked to form a 3D array of voxels
containing nodule candidate predictions.