Over the past several chapters, we have built a decent number of systems that are important components of our project. We started loading our data, built and improved classifiers for nodule candidates, trained segmentation models to find those candidates, handled the support infrastructure needed to train and evaluate those models, and started saving the results of our training to disk. Now it’s time to unify the components we have into a cohesive whole, so that we may realize the full goal of our project: it’s time to automatically detect cancer.
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.