Deep-Learning-with-PyTorch

280 CHAPTER 11 Training a classification model to detect suspected tumors
11.1 A foundational model and training loop
We’re going to do two main things in this chapter. We’ll start by building the nodule
classification model and training loop that will be the foundation that the rest of part 2
uses to explore the larger project. To do that, we’ll use the Ct and LunaDataset classes
we implemented in chapter 10 to feed DataLoader instances. Those instances, in turn,
will feed our classification model with data via training and validation loops.
We’ll finish the chapter by using the results from running that training loop to introduce
one of the hardest challenges in this part of the book: how to get high-quality
results from messy, limited data. In later chapters, we’ll explore the specific ways in
which our data is limited, as well as mitigate those limitations.
Let’s recall our high-level roadmap from chapter 9, shown here in figure 11.1.
Right now, we’ll work on producing a model capable of performing step 4: classification.
As a reminder, we will classify candidates as nodules or non-nodules (we’ll build
another classifier to attempt to tell malignant nodules from benign ones in chapter
14). That means we’re going to assign a single, specific label to each sample that we
present to the model. In this case, those labels are “nodule” and “non-nodule,” since
each sample represents a single candidate.
Getting an early end-to-end version of a meaningful part of your project is a great
milestone to reach. Having something that works well enough for the results to be
evaluated analytically let’s you move forward with future changes, confident that you
Step 1 (ch. 10):
Data Loading
STep 2 (ch. 13):
Segmentation
Step 4 (ch. 11+12):
ClaSsification
.MHD
.RAW
CT
Data
segmentation
model
Step 3 (ch. 14):
Grouping
candidate
Locations
[(I,R,C),
(I,R,C),
(I,R,C),
...
]
candidate
Sample
ClaSsification
model
[NEG,
p=0.1
POS,
p=0.9
NEG,
p=0.2
...
]
Step
5 (ch. 14):
Nodule analysis
and Diagnosis
MAL/BEN
p=0.9
Figure 11.1 Our end-to-end project to detect lung cancer, with a focus on this chapter’s topic:
step 4, classification