Deep-Learning-with-PyTorch

Bridging CT segmentation and nodule candidate classification
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origin_xyz=ct.origin_xyz,
vxSize_xyz=ct.vxSize_xyz,
)
cls_tup = (prob_nodule, prob_mal, center_xyz, center_irc)
classifications_list.append(cls_tup)
return classifications_list
This is great! We can now threshold the output probabilities to get a list of things our
model thinks are actual nodules. In a practical setting, we would probably want to output
them for a radiologist to inspect. Again, we might want to adjust the threshold to
err a bit on the safe side: that is, if our threshold was 0.3 instead of 0.5, we would present
a few more candidates that turn out not to be nodules, while reducing the risk of
missing actual nodules.
Listing 14.6
nodule_analysis.py:333, NoduleAnalysisApp.main
If we don’t pass run_validation, we
print individual information …
if not self.cli_args.run_validation:
print(f"found nodule candidates in {series_uid}:")
for prob, prob_mal, center_xyz, center_irc in classifications_list:
if prob > 0.5:
s = f"nodule prob {prob:.3f}, "
if self.malignancy_model:
s += f"malignancy prob {prob_mal:.3f}, "
s += f"center xyz {center_xyz}"
print(s)
if series_uid in candidateInfo_dict:
one_confusion = match_and_score(
classifications_list, candidateInfo_dict[series_uid]
)
all_confusion += one_confusion
print_confusion(
series_uid, one_confusion, self.malignancy_model is not None
)
print_confusion(
"Total", all_confusion, self.malignancy_model is not None
)
Let’s run this for a given CT from the validation set: 3
… for all candidates found by
the segmentation where the
classifier assigned a nodule
probability of 50% or more.
If we have the ground truth data, we
compute and print the confusion matrix and
also add the current results to the total.
$ python3.6 -m p2ch14.nodule_analysis 1.3.6.1.4.1.14519.5.2.1.6279.6001
➥ .592821488053137951302246128864
...
found nodule candidates in 1.3.6.1.4.1.14519.5.2.1.6279.6001.5928214880
➥ 53137951302246128864:
3
We chose this series specifically because it has a nice mix of results.