Deep-Learning-with-PyTorch

Summary
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14.9 Exercises
14.10 Summary
1 Implement a test set for classification, or reuse the test set from chapter 13’s
exercises. Use the validation set to pick the best epochs while training, but use
the test set to evaluate the end-to-end project. How well does performance on
the validation set line up with performance on the test set?
2 Can you train a single model that is able to do three-way classification, distinguishing
among non-nodules, benign modules, and malignant nodules in one
pass?
a What class-balancing split works best for training?
b How does this single-pass model perform, compared to the two-pass
approach we are using in the book?
3 We trained our classifier on annotations, but expect it to perform on the output
of our segmentation. Use the segmentation model to build a list of non-nodules
to use during training instead of the non-nodules provided.
a Does the classification model performance improve when trained on this
new set?
b Can you characterize what kinds of nodule candidates see the biggest
changes with the newly trained model?
4 The padded convolutions we use result in less than full context near the edges
of the image. Compute the loss for segmented pixels near the edges of the CT
scan slice, versus those in the interior. Is there a measurable difference between
the two?
5 Try running the classifier on the entire CT by using overlapping 32 × 48 × 48
patches. How does this compare to the segmentation approach?
• An unambiguous split between training and validation (and test) sets is crucial.
Here, splitting by patient is much less prone to getting things wrong. This is
even more true when you have several models in your pipeline.
• Getting from pixel-wise marks to nodules can be achieved using very traditional
image processing. We don’t want to look down on the classics, but value these
tools and use them where appropriate.
• Our diagnosis script performs both segmentation and classification. This allows
us to diagnose a CT that we have not seen before, though our current Dataset
implementation is not configured to accept series_uids from sources other
than LUNA.
• Fine-tuning is a great way to fit a model while using a minimum of training data.
Make sure the pretrained model has features relevant to your task, and make
sure that you retrain a portion of the network with enough capacity.