Deep Generative Models, and Data Augmentation, Labelling, and Imperfections

The book "Deep Generative Models, and Data Augmentation, Labelling, and Imperfections" documents the refereed proceedings of the first MICCAI workshops on Deep Generative Models (DG4MICCAI 2021) and on Data Augmentation, Labelling, and Imperfections (DALI 2021), which took place in October 2021 as part of the MICCAI conference. Originally planned to be held in Strasbourg, France, the workshops were conducted virtually due to the COVID-19 pandemic. The contributions in this volume include 12 accepted papers out of 17 submissions for DG4MICCAI 2021, focusing on the latest algorithmic developments and promising future directions in the field of Deep Generative Models. These models, including Generative Adversarial Networks (GAN) and Variational Auto-Encoders (VAE), are gaining increasing attention in computer vision, machine learning, as well as in medical imaging and computer-assisted intervention. For DALI 2021, 15 out of 32 submitted papers were accepted, addressing the rigorous examination of medical data in relation to machine learning systems.