Differently from CNNs which only exploit local features in each level, transformers are designed to capture a long range of information. This aspect is currently receiving great interest in the medical imaging field, where the global information of images plays a key role in the diagnosis, prognosis and treatment of diseases.
The aim of this Special Issue is to collect contributions which take into account the current advances in the application of transformer models in the medical imaging field, with the intent of expanding the state-of-the-art and proposing novel approaches exploring the limitations and advantages of these novel emerging architectures.
