A gentle introduction to deep learning in medical image processing

Important architectures in deep learning

With the developments of the previous section, much progress was made towards improved signal, image, video, and audio processing, as already detailed earlier. In this intro- duction, we are not able to highlight all developments, because this would go well beyond the scope of this document, and there are other sources that are more suited for this pur- pose [31,37,12]. Instead, we will only shortly discuss some advanced network architectures that we believe had, or will have, an impact on medical image processing.

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Autoencoders use a contracting and an expanding branch to find representations of the input of a lower dimensional- ity [41]. They do not require annotations, as the network is trained to predict the original input using loss functions such as L(θ) f^ˆ (x) x ². Variants use convolutional networks [42], add noise to the input [43], or aim at finding sparse representations [44].
Generative adversarial networks (GANs) employ two networks to learn a representative distribution from the train- ing data [45]. A generator network creates new images from a noise input, while a discriminator network tries to differen- tiate real images from generated images. Both are trained in an alternating manner such that both gradually improve for their respective tasks. GANs are known to generate plausible and realistically looking images. So-called Wasserstein GANs can reduce instability in training [46]. Conditional GANs [47]

allow to encode states in the process such that images with desired properties can be generated. CycleGANs [48] drive this even further as they allow to convert one image from one domain to another, for example from day to night, without directly corresponding images in the training data.