About this Research Topic
In cardiovascular medicine, GANs are increasingly adopted in a wide range of applications in cardiovascular imaging, electrocardiography signals, and patient characteristics. This topic focuses on novel GAN approaches and applications to various cardiovascular research, including but not limited to domain transfer, domain adaption, dose reduction, missing modality, data augmentation, image reconstruction, synthesis, segmentation, detection, and classification. It also aims to address the technical challenges of GANs in model training, validation, transparency, and robustness.
We welcome researchers with a background in machine learning, basic science, clinical cardiology, or cross-disciplinary field to contribute high-quality papers in methodology, original investigations, clinical applications, ethics, reviews, and mini-reviews.
Keywords: Generative Adversarial Networks
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.