SPIE - Education SC1262

This half-day deep dive course will guide researchers with some background knowledge, e.g. from the introductory course, SC1235 Introduction to Medical Image Analysis using Convolutional Neural Networks, through the latest literature of generative adversarial networks (GANs) and their application to medical data. First and foremost, GANs are powerful appearance models, and thus inherently bring a deep understanding of their respective domain. However, GANs can also be used to map between different domains (such as between CT and MRI) or to help training better segmentation models. Adversarial training can be introduced into several learning tasks in medical image analysis. It has been shown to help make image analysis algorithms more robust to variability in the data and to reduce the probability of failure on unseen cases. GANs in their initial implementation have been known to be hard to configure and train, but recent advances have helped them catch ground in applications of classification and segmentation, without requiring too much "witchcraft". We will introduce GANs, give an overview of their development towards the state of the art, and explain specific architectural decisions and developments that have been introduced to stabilize their training (CycleGAN, Wasserstein based loss). We will show code examples and illustrate the course content with live demonstrations on downsampled data, so that the participants gain some first-hand experience on the subject.