chapter eleven

11 Using PyTorch to fight cancer

 

This chapter covers

  • Breaking a large problem into smaller, easier ones
  • Exploring an intricate deep learning problem, and deciding on a structure and approach
  • Downloading the training data

We have two main goals for this chapter. We’ll start by covering the overall plan for part 2 of the book so that we have a solid idea of the larger scope the following individual chapters will be building toward. In chapter 12, we will begin to build out the data-parsing and data manipulation routines that will produce data to be consumed in chapter 13, while training our first model. To do what’s needed for those upcoming chapters well, we’ll also use this chapter to cover some of the context in which our project will be operating: we’ll go over data formats and data sources and explore the constraints that our problem domain place on us. Get used to performing these tasks, since you’ll have to do them for any serious deep learning project.

11.1 Introduction to the use case

11.2 Preparing for a large-scale project

11.3 What is a CT scan, exactly?

11.4 The project: An end-to-end detector for lung cancer

11.4.1 Why can’t we just throw data at a neural network until it works?

11.4.2 Our data source: The LUNA Grand Challenge

11.4.3 Downloading the LUNA data

11.5 Conclusion

Summary