Guest Editorial Generative Adversarial Networks in Biomedical Image Computing

The papers in this special section focus on generative adversarial networks in biomedical image computing. The field of biomedical imaging has obtained great progress from Roentgen’s original discovery of the X-ray to the current imaging tools, including Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), Computed Tomography (CT), and Ultrasound (US). The benefits of using these non-invasive imaging technologies are to assess the current condition of an organ or tissue, which can be used to monitor a patient over time over time for accurate and timely diagnosis and treatment.With the development of imaging technologies, developing advanced artificial intelligence algorithms for automated image analysis has shown the potential to change many aspects of clinical applications within the next decade. Meanwhile, these advanced technologies have also brought new issues and challenges. Thus, there has been a growing demand for biomedical imaging computing to be a component of clinical trials and device improvement. Currently, Generative adversarial networks (GANs) have been attached growing interests in the computer vision community due to their capability of data generation or translation. GAN-based models are able to learn from a set of training data and generate new data with the same characteristics as the training ones, which have also proven to be the state of the art for generating sharp and realistic images. More importantly, GAN has been rapidly applied to many traditional and novel applications in the medical domain, such as image reconstruction, segmentation, diagnosis, synthesis, and so on. Despite GAN substantial progress in these areas, their application to medical image computing still faces challenges and unsolved problems remain.