Deep-Learning-with-PyTorch

Preparing for a large-scale project
237
reasonable solutions. That attention on the problem has also resulted in a lot of highquality
papers and open source projects, which are a great source of inspiration and
ideas. This will be a huge help once we conclude part 2 of the book, if you are interested
in continuing to improve on the solution we create. We’ll provide some links to
additional information in chapter 14.
This part of the book will remain focused on the problem of detecting lung
tumors, but the skills we’ll teach are general. Learning how to investigate, preprocess,
and present your data for training is important no matter what project you’re working
on. While we’ll be covering preprocessing in the specific context of lung tumors, the
general idea is that this is what you should be prepared to do for your project to succeed.
Similarly, setting up a training loop, getting the right performance metrics, and tying
the project’s models together into a final application are all general skills that we’ll
employ as we go through chapters 9 through 14.
NOTE While the end result of part 2 will work, the output will not be accurate
enough to use clinically. We’re focusing on using this as a motivating example
for teaching PyTorch, not on employing every last trick to solve the problem.
9.2 Preparing for a large-scale project
This project will build off of the foundational skills learned in part 1. In particular, the
content covering model construction from chapter 8 will be directly relevant.
Repeated convolutional layers followed by a resolution-reducing downsampling layer
will still make up the majority of our model. We will use 3D data as input to our
model, however. This is conceptually similar to the 2D image data used in the last few
chapters of part 1, but we will not be able to rely on all of the 2D-specific tools available
in the PyTorch ecosystem.
The main differences between the work we did with convolutional models in chapter
8 and what we’ll do in part 2 are related to how much effort we put into things outside
the model itself. In chapter 8, we used a provided, off-the-shelf dataset and did
little data manipulation before feeding the data into a model for classification. Almost
all of our time and attention were spent building the model itself, whereas now we’re
not even going to begin designing the first of our two model architectures until chapter
11. That is a direct consequence of having nonstandard data without prebuilt
libraries ready to hand us training samples suitable to plug into a model. We’ll have to
learn about our data and implement quite a bit ourselves.
Even when that’s done, this will not end up being a case where we convert the CT to
a tensor, feed it into a neural network, and have the answer pop out the other side. As
is common for real-world use cases such as this, a workable approach will be more complicated
to account for confounding factors such as limited data availability, finite
computational resources, and limitations on our ability to design effective models. Please
keep that in mind as we build to a high-level explanation of our project architecture.
Speaking of finite computational resources, part 2 will require access to a GPU to
achieve reasonable training speeds, preferably one with at least 8 GB of RAM. Trying