Deep-Learning-with-PyTorch

The project: An end-to-end detector for lung cancer
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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 9.7 The end-to-end process of taking a full-chest CT scan and determining whether the patient has a
malignant tumor
9.4.2 What is a nodule?
As we’ve said, in order to understand our data well enough to use it effectively, we
need to learn some specifics about cancer and radiation oncology. One last key thing
we need to understand is what a nodule is. Simply put, a nodule is any of the myriad
lumps and bumps that might appear inside someone’s lungs. Some are problematic
from a health-of-the-patient perspective; some are not. The precise definition 4 limits
the size of a nodule to 3 cm or less, with a larger lump being a lung mass; but we’re
going to use nodule interchangeably for all such anatomical structures, since it’s a
somewhat arbitrary cutoff and we’re going to deal with lumps on both sides of 3 cm
using the same code paths. A nodule—a small mass in the lung—can turn out to be
benign or a malignant tumor (also referred to as cancer). From a radiological perspective,
a nodule is really similar to other lumps that have a wide variety of causes: infection,
inflammation, blood-supply issues, malformed blood vessels, and diseases other
than tumors.
4 Eric J. Olson, “Lung nodules: Can they be cancerous?” Mayo Clinic, http://mng.bz/yyge.