Deep-Learning-with-PyTorch

Using segmentation
to find suspected nodules
This chapter covers
• Segmenting data with a pixel-to-pixel model
• Performing segmentation with U-Net
• Understanding mask prediction using Dice loss
• Evaluating a segmentation model’s performance
In the last four chapters, we have accomplished a lot. We’ve learned about CT scans
and lung tumors, datasets and data loaders, and metrics and monitoring. We have
also applied many of the things we learned in part 1, and we have a working classifier.
We are still operating in a somewhat artificial environment, however, since we
require hand-annotated nodule candidate information to load into our classifier.
We don’t have a good way to create that input automatically. Just feeding the entire
CT into our model—that is, plugging in overlapping 32 × 32 × 32 patches of data—
would result in 31 × 31 × 7 = 6,727 patches per CT, or about 10 times the number of
annotated samples we have. We’d need to overlap the edges; our classifier expects
the nodule candidate to be centered, and even then the inconsistent positioning
would probably present issues.
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