Deep-Learning-with-PyTorch

426 CHAPTER 14 End-to-end nodule analysis, and where to go next
losses stagnating is qualitatively similar. Back then, case A indicated that we did not have
enough capacity, so we should consider the following three possible causes:
• Features (the output of the last convolution) obtained by training the network
on nodule versus non-nodule classification are not useful for malignancy detection.
• The capacity of the head—the only part we are training—is not large enough.
• The network might have too little capacity overall.
depth 1 training
depth 1 validation
depth 1 validation
depth 1 training
Figure 14.10
AUC (left) and loss (right) for the fine-tuning of the last linear layer
If training only the fully connected part in fine-tuning is not enough, the next thing to
try is to include the last convolutional block in the fine-tuning training. Happily, we
introduced a parameter for that, so we can include the block4 part into our training:
python3 -m p2ch14.training \
--malignant \
--dataset MalignantLunaDataset \
--finetune data/part2/models/cls_2020-02-06_14.16.55_final-nodule-
➥ nonnodule.best.state \
--finetune-depth 2 \
--epochs 10 \
malben-finetune-twolayer
This CLI
parameter is new.
Once done, we can check our new best model against the baseline. Figure 14.11 looks
more reasonable! We flag about 75% of the malignant nodules with almost no false
positives. This is clearly better than the 65% the diameter baseline can give us. Trying
to push beyond 75%, our model’s performance falls back to the baseline. When we go
back to the classification problem, we will want to pick a point on the ROC curve to
balance true positives versus false positives.
We are roughly on par with the baseline, and we will be content with that. In section
14.7, we hint at the many things that you can explore to improve these results,
but that didn’t fit in this book.