Deep-Learning-with-PyTorch

Summary
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work we’ve done will pay off handsomely as we move forward. We’ll see those dividends
shortly, once we start implementing our data-loading routines in chapter 10.
Since this chapter has been informational only, without any code, we’ll skip the
exercises for now.
9.6 Summary
• Our approach to detecting cancerous nodules will have five rough steps: data loading,
segmentation, grouping, classification, and nodule analysis and diagnosis.
• Breaking down our project into smaller, semi-independent subprojects makes
teaching each subproject easier. Other approaches might make more sense for
future projects with different goals than the ones for this book.
• A CT scan is a 3D array of intensity data with approximately 32 million voxels,
which is around a million times larger than the nodules we want to recognize.
Focusing the model on a crop of the CT scan relevant to the task at hand will
make it easier to get reasonable results from training.
• Understanding our data will make it easier to write processing routines for our
data that don’t distort or destroy important aspects of the data. The array of CT
scan data typically will not have cubic voxels; mapping location information in
real-world units to array indexes requires conversion. The intensity of a CT scan
corresponds roughly to mass density but uses unique units.
• Identifying the key concepts of a project and making sure they are well represented
in our design can be crucial. Most aspects of our project will revolve
around nodules, which are small masses in the lungs and can be spotted on a
CT along with many other structures that have a similar appearance.
• We are using the LUNA Grand Challenge data to train our model. The LUNA
data contains CT scans, as well as human-annotated outputs for classification and
grouping. Having high-quality data has a major impact on a project’s success.