Figure 1.

Distribution of publications on deep generative models applied to medical imaging data augmentation as of 2022. (a) The number of publications per architecture type and year. (b) The distribution of publications by modality, with CT and MRI being the most-commonly studied imaging modalities. Note that for cross-modal translation tasks, both the source and target modalities are counted in this plot. (c) The distribution of publications by downstream task, with segmentation and classification being the most common tasks in medical imaging. This figure illustrates the increasing interest in using deep generative models for data augmentation in medical imaging and highlights the diversity of tasks and modalities that have been addressed in the literature.