Deep-Learning-with-PyTorch

406 CHAPTER 14 End-to-end nodule analysis, and where to go next
1. Nodule Candidate Generation
1a. Segmentation
n 1b. Grouping 1c. sample tuples
((...,(..., IRC),
...
(..., IRC))
2. Nodule and malignancy claSsification
2a. Nodule claSsification 2b. ROC/AUC Metrics 2c. fine-tuning malignancy model
3. End-to-end detection
3a. (..., IRC) 3b. Is nodule? 3c. is Malignant?
Figure 14.2
A detailed look at the work remaining for our end-to-end project
b Grouping—We will group the voxels into nodule candidates by applying a threshold
to the predictions, and then grouping connected regions of flagged voxels.
c Constructing sample tuples—Each identified nodule candidate will be used to
construct a sample tuple for classification. In particular, we need to produce
the coordinates (index, row, column) of that nodule’s center.
Once this is achieved, we will have an application that takes a raw CT scan from a
patient and produces a list of detected nodule candidates. Producing such a list is the
task in the LUNA challenge. If this project were to be used clinically (and we
reemphasize that our project should not be!), this nodule list would be suitable for
closer inspection by a doctor.
2 Classify nodules and malignancy. We’ll take the nodule candidates we just produced
and pass them to the candidate classification step we implemented in
chapter 12, and then perform malignancy detection on the candidates flagged
as nodules:
a Nodule classification—Each nodule candidate from segmentation and grouping
will be classified as either nodule or non-nodule. Doing so will allow us to
screen out the many normal anatomical structures flagged by our segmentation
process.