A gentle introduction to deep learning in medical image processing

Variational networks enable the conversion of an energy minimization problem into a neural network structure [55]. We consider this type of network as particular interesting, as many problems in traditional medical image processing are expressed as energy minimization problems. The main idea is as follows: The energy function is typically mini- mized by optimization programs such as gradient descent. Thus, we are able to use the gradient of the original problem to construct a so-called variational unit that describes exactly one update step of the optimization program. Succession of such units then describe the complete variational network. Two observations are noteworthy: First, this type of frame- work allows to learn operators within one variational unit, such as a sparsifying transform for compressed sensing prob- lems. Second, the variational units generally form residual blocks, and thus variational networks are always ResNets as well.
Recurrent neural networks (RNNs) enable the processing of sequences with long term dependencies [56]. Furthermore,
recurrent nets introduce state variables that allow the cells to carry memory and essentially model any finite state machine. Extensions are long-short-term memory (LSTM) networks
[57] and gated recurrent units (GRU) [58] that can model explicit read and write memory transactions similar to a com- puter.

3. Advanced deep learning concepts

In addition to the above mentioned architectures, there are also useful concepts that allow building more robust and ver- satile networks. Again, the here listed methods are incomplete. Still, we aimed at including the most useful ones.

Data augmentation In data augmentation, common sources of variation are explicitly added to training samples. These models of variation typically include noise, changes in contrast, and rotations and translations. In biased data, it can be used to improve the numbers of infrequent observa- tions. In particular, the success of U-net is also related to very powerful augmentation techniques that include, for example, non-rigid deformations of input images and the desired seg- mentation [50]. In most recent literature, reports are found that also GANs are useful for data augmentation [59].

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