Lecture Notes in Deep Learning: Weakly and Self-supervised Learning – Part 2
From 2-D to 3-D Annotations
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Welcome back to deep learning! So today, we want to continue talking about weekly annotating examples. Today’s topic will be particularly focusing on 3-D annotations.
So welcome back to our lectures on weakly and self-supervised learning, the second part: From sparse annotations to dense segmentations. Now, what means dense, here? “You dense …”. Probably not.
What we are actually interested in is a dense 3-D segmentation. Here, you have an example of a volumetric image and you can see that we have a couple of slices that we visualize on the left-hand side. Then, we can annotate each of these slices, and then we can use them for training, for example, of a 3-D U-net to produce a full 3-D segmentation. As you already have might have guessed annotating all of the slices subsequently probably with different orientations in order to get at rid of bias that is introduced by the slice orientation is extremely tedious. So, you don’t want to do that. What we will look into in the next couple of minutes is talking about how to use sparsely sampled slices in order to get a full automatic 3-D segmentation. Also, this approach is interesting because it allows for interactive correction.
Let’s look into this idea. You train with sparse labels. Typically, we have these one-hot labels, essentially being one if you are part of the segmentation mask. The mask is either true or false. Then, you get this cross-entropy loss where you essentially then backpropagate with. You use exactly the label that returned one because it was annotated. But, of course, that’s not true for the non annotated samples. What you can do is you can develop this further to something that is called a weighted cross-entropy loss. Here, you multiply the original cross-entropy with an additional weight w. w is set in a way that it’s zero if it’s not labeled. You can assign a weight that’s greater than zero otherwise. By the way, if you have this w, you can also extend it to be interactive by updating y and the w(y). So, this means that you update the labels over the iterations with users. If you do so then, you can actually work with sparsely annotated 3-D volumes and train algorithms to produce complete 3-D segmentations.
Let’s look at some takeaway messages. Weakly supervised learning is actually an approach to omit fine-grained labels because they’re expensive. We try to get away with something that is much cheaper. The core idea is that the label has less detail than the target and the methods essentially depend on prior knowledge, like knowledge about the object, knowledge about the distribution, or even a prior algorithm that can help you with the refinement of the labels, and weak labels that we called hints earlier.
Typically this is inferior to fully supervised training, but it’s highly relevant in practice because annotations are very very costly. Don’t forget about transfer learning. This can also help you. We discussed this already quite a bit in earlier lectures. What we’ve seen here is, of course, related to semi-supervised learning and self-supervised learning.
This is also the reason why we talk next time about the topic of self-supervision and how these ideas have sparked quite some boost in the field over the last couple of years. So, thank you very much for listening and looking forward to seeing you in the next video. Bye-bye!
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