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.