Deep-Learning-with-PyTorch

378 CHAPTER 13 Using segmentation to find suspected nodules
isMal_bool
)
)
False,
isNodule_bool
False,
False, hasAnnotation_bool
0.0,
series_uid,
candidateCenter_xyz,
Other than the addition of the hasAnnotation_bool and isMal_bool flags (which we
won’t use in this chapter), the new annotations will slot in and be usable just like the
old ones.
NOTE You might be wondering why we haven’t discussed the LIDC before
now. As it turns out, the LIDC has a large amount of tooling that’s already
been constructed around the underlying dataset, which is specific to the
LIDC. You could even get ready-made masks from PyLIDC. That tooling presents
a somewhat unrealistic picture of what sort of support a given dataset
might have, since the LIDC is anomalously well supported. What we’ve done
with the LUNA data is much more typical and provides for better learning,
since we’re spending our time manipulating the raw data rather than learning
an API that someone else cooked up.
13.5.4 Implementing Luna2dSegmentationDataset
Compared to previous chapters, we are going to take a different approach to the training
and validation split in this chapter. We will have two classes: one acting as a general
base class suitable for validation data, and one subclassing the base for the training
set, with randomization and a cropped sample.
While this approach is somewhat more complicated in some ways (the classes
aren’t perfectly encapsulated, for example), it actually simplifies the logic of selecting
randomized training samples and the like. It also becomes extremely clear which code
paths impact both training and validation, and which are isolated to training only.
Without this, we found that some of the logic can become nested or intertwined in
ways that make it hard to follow. This is important because our training data will look
significantly different from our validation data!
NOTE Other class arrangements are also viable; we considered having two
entirely separate Dataset subclasses, for example. Standard software engineering
design principles apply, so try to keep your structure relatively simple,
and try to not copy and paste code, but don’t invent complicated
frameworks to prevent having to duplicate three lines of code.
The data that we produce will be two-dimensional CT slices with multiple channels.
The extra channels will hold adjacent slices of CT. Recall figure 4.2, shown here as
figure 13.12; we can see that each slice of CT scan can be thought of as a 2D grayscale
image.