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Convolutional Neural Networks (CNNs) can play a key role in Medical ImageAnalysis under large-scale annotated datasets. However, preparing such massivedataset is demanding. In this context, Generative Adversarial Networks (GANs)can generate realistic but novel samples, and thus effectively cover the realimage distribution. In terms of interpolation, the GAN-based medical imageaugmentation is reliable because medical modalities can display the humanbody's strong anatomical consistency at fixed position while clearly reflectinginter-subject variability; thus, we propose to use noise-to-image GANs (e.g.,random noise samples to diverse pathological images) for (i) medical DataAugmentation (DA) and (ii) physician training. Regarding the DA, theGAN-generated images can improve Computer-Aided Diagnosis based on supervisedlearning. For the physician training, the GANs can display novel desiredpathological images and help train medical trainees despiteinfrastructural/legal constraints. This thesis contains four GAN projectsaiming to present such novel applications' clinical relevance in collaborationwith physicians. Whereas the methods are more generally applicable, this thesisonly explores a few oncological applications.

Pathology-Aware Generative Adversarial Networks for Medical Image Augmentation