  Convolutional Neural Networks (CNNs) can play a key role in Medical Image
Analysis under large-scale annotated datasets. However, preparing such massive
dataset is demanding. In this context, Generative Adversarial Networks (GANs)
can generate realistic but novel samples, and thus effectively cover the real
image distribution. In terms of interpolation, the GAN-based medical image
augmentation is reliable because medical modalities can display the human
body's strong anatomical consistency at fixed position while clearly reflecting
inter-subject variability; thus, we propose to use noise-to-image GANs (e.g.,
random noise samples to diverse pathological images) for (i) medical Data
Augmentation (DA) and (ii) physician training. Regarding the DA, the
GAN-generated images can improve Computer-Aided Diagnosis based on supervised
learning. For the physician training, the GANs can display novel desired
pathological images and help train medical trainees despite
infrastructural/legal constraints. This thesis contains four GAN projects
aiming to present such novel applications' clinical relevance in collaboration
with physicians. Whereas the methods are more generally applicable, this thesis
only explores a few oncological applications.
