Deep-Learning-with-PyTorch

Part 2
Learning from images
in the real world: Early
detection of lung cancer
Part 2 is structured differently than part 1; it’s almost a book within a book.
We’ll take a single use case and explore it in depth over the course of several chapters,
starting with the basic building blocks we learned in part 1, and building out
a more complete project than we’ve seen so far. Our first attempts are going to be
incomplete and inaccurate, and we’ll explore how to diagnose those problems
and then fix them. We’ll also identify various other improvements to our solution,
implement them, and measure their impact. In order to train the models we’ll
develop in part 2, you will need access to a GPU with at least 8 GB of RAM as well
as several hundred gigabytes of free disk space to store the training data.
Chapter 9 introduces the project, environment, and data we will consume and
the structure of the project we’ll implement. Chapter 10 shows how we can turn
our data into a PyTorch dataset, and chapters 11 and 12 introduce our classification
model: the metrics we need to gauge how well the dataset is training, and
implement solutions to problems preventing the model from training well. In
chapter 13, we’ll shift gears to the beginning of the end-to-end project by creating
a segmentation model that produces a heatmap rather than a single classification.
That heatmap will be used to generate locations to classify. Finally, in chapter
14, we’ll combine our segmentation and classification models to perform a
final diagnosis.